Papers

The following preprints are provided here to allow for a deeper view of our research work, as well as to promote the rapid dissemination of research results. Please consider, on the other hand, that these preprints can differ from their published version in ways that may not be entirely negligible, and for this reason we recommend you to refer to the published version whenever they have to be used or cited.
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2024

D'Agostino, A., Garbazza, C., Malpetti, D., Azzimonti, L., Mangili, F., Stein, H., del Giudice, R., Cicolin, A., Cirignotta, F., Manconi, M., Aquilino, D., Baiardi, S., Bianconcini, A., Canevini, M., Cicolin, A., Cirignotta, F., D'Agostino, A., Giudice, R.D., Fanti, V., Filippakos, F., Fior, G., Fonti, C., Furia, F., Gambini, O., Garbazza, C., Giordano, A., Giordano, B., Manconi, M., Marconi, A.M., Martini, A., Mondini, S., Piazza, N., Raimondo, E., Riccardi, S., Rizzo, N., Santoro, R., Serrati, C., Simonazzi, G., Stein, H., Zambrelli, E. (2024). Optimal risk and diagnosis assessment strategies in perinatal depression: a machine learning approach from the life-ON study cohort. Psychiatry Research 332, 115687.

Optimal risk and diagnosis assessment strategies in perinatal depression: a machine learning approach from the life-ON study cohort

@ARTICLE{malpetti2024a,
   title = {Optimal risk and diagnosis assessment strategies in perinatal depression: a machine learning approach from the life-{ON} study cohort},
   journal = {Psychiatry Research},
   volume = {332},
   author = {D'Agostino, A. and Garbazza, C. and Malpetti, D. and Azzimonti, L. and Mangili, F. and Stein, H. and del Giudice, R. and Cicolin, A. and Cirignotta, F. and Manconi, M. and Aquilino, D. and Baiardi, S. and Bianconcini, A. and Canevini, M. and Cicolin, A. and Cirignotta, F. and D'Agostino, A. and Giudice, R.D. and Fanti, V. and Filippakos, F. and Fior, G. and Fonti, C. and Furia, F. and Gambini, O. and Garbazza, C. and Giordano, A. and Giordano, B. and Manconi, M. and Marconi, A.M. and Martini, A. and Mondini, S. and Piazza, N. and Raimondo, E. and Riccardi, S. and Rizzo, N. and Santoro, R. and Serrati, C. and Simonazzi, G. and Stein, H. and Zambrelli, E.},
   pages = {115687},
   year = {2024},
   doi = {10.1016/j.psychres.2023.115687},
   url = {}
}
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Antonucci, A., Piqué, G., Zaffalon, M. (2024). Zero-shot causal graph extrapolation from text via LLMs. In First XAI4Sci Workshop on Explainable Machine Learning for Sciences (@AAAI '24).

Zero-shot causal graph extrapolation from text via LLMs

@INPROCEEDINGS{antonucci2023a,
   title = {Zero-shot causal graph extrapolation from text via {LLMs}},
   booktitle = {First {XAI4Sci} Workshop on Explainable Machine Learning for Sciences ({@AAAI} '24)},
   author = {Antonucci, A. and Piqu\'e, G. and Zaffalon, M.},
   year = {2024},
   doi = {},
   url = {http://arxiv.org/abs/2312.14670}
}
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Manconi, M., van der Gaag, L.C., Mangili, F., Garbazza, C., Riccardi, S., Cajochen, C., Mondini, S., Furia, F., Zambrelli, E., Baiardi, S., Giordano, A., Rizzo, N., Fonti, C., Viora, E., D'Agostino, A., Cicolin, A., Cirignotta, F., Aquilino, D., Barassi, A., del Giudice, R., Fior, G., Gambini, O., Giordano, B., Martini, A., Serrati, C., Stefanelli, R., Scarone, S., Canevini, M., Fanti, V., Stein, H., Marconi, A.M., Raimondo, E., Viglietta, E., Santoro, R., Simonazzi, G., Bianconcini, A., Meani, F., Piazza, N., Filippakos, F., Gyr, T. (2024). Sleep and sleep disorders during pregnancy and postpartum: The Life-ON study. Sleep Medicine 113, pp. 41–48.

Sleep and sleep disorders during pregnancy and postpartum: The Life-ON study

@ARTICLE{mangili2023a,
   title = {Sleep and sleep disorders during pregnancy and postpartum: {T}he {L}ife-{ON} study},
   journal = {Sleep Medicine},
   volume = {113},
   author = {Manconi, M. and van der Gaag, L.C. and Mangili, F. and Garbazza, C. and Riccardi, S. and Cajochen, C. and Mondini, S. and Furia, F. and Zambrelli, E. and Baiardi, S. and Giordano, A. and Rizzo, N. and Fonti, C. and Viora, E. and D'Agostino, A. and Cicolin, A. and Cirignotta, F. and Aquilino, D. and Barassi, A. and del Giudice, R. and Fior, G. and Gambini, O. and Giordano, B. and Martini, A. and Serrati, C. and Stefanelli, R. and Scarone, S. and Canevini, M. and Fanti, V. and Stein, H. and Marconi, A.M. and Raimondo, E. and Viglietta, E. and Santoro, R. and Simonazzi, G. and Bianconcini, A. and Meani, F. and Piazza, N. and Filippakos, F. and Gyr, T.},
   pages = {41--48},
   year = {2024},
   doi = {doi.org/10.1016/j.sleep.2023.10.021},
   url = {}
}
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Zaffalon, M., Antonucci, A., Cabañas, R., Huber, D., Azzimonti, D. (2024). Efficient computation of counterfactual bounds. International Journal of Approximate Reasoning, 109111.

Efficient computation of counterfactual bounds

@ARTICLE{zaffalon2023b,
   title = {Efficient computation of counterfactual bounds},
   journal = {International Journal of Approximate Reasoning},
   author = {Zaffalon, M. and Antonucci, A. and Caba\~nas, R. and Huber, D. and Azzimonti, D.},
   pages = {109111},
   year = {2024},
   doi = {10.1016/j.ijar.2023.109111},
   url = {}
}
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Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting.

Properties of the reconciled distributions for Gaussian and count forecasts

@ARTICLE{zambon2024b,
   title = {Properties of the reconciled distributions for {G}aussian and count forecasts},
   journal = {International Journal of Forecasting},
   author = {Zambon, L. and Agosto, A. and Giudici, P. and Corani, G.},
   year = {2024},
   doi = {10.1016/j.ijforecast.2023.12.004},
   url = {}
}
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Zambon, L., Azzimonti, D., Corani, G. (2024). Efficient probabilistic reconciliation of forecasts for real-valued and count time series. Statistics and Computing 32(1), 21.

Efficient probabilistic reconciliation of forecasts for real-valued and count time series

@ARTICLE{zambon2024a,
   title = {Efficient probabilistic reconciliation of forecasts for real-valued and count time series},
   journal = {Statistics and Computing},
   volume = {32},
   author = {Zambon, L. and Azzimonti, D. and Corani, G.},
   number = {1},
   pages = {21},
   year = {2024},
   doi = {10.1007/s11222-023-10343-y},
   url = {}
}
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2023

Adorni, G., Mangili, F., Piatti, A., Bonesana, C., Antonucci, A. (2023). Rubric-based learner modelling via noisy gates Bayesian networks for computational thinking skills assessment. Journal of Communications Software and System 19(1), pp. 52–64.

Rubric-based learner modelling via noisy gates Bayesian networks for computational thinking skills assessment

@ARTICLE{Adorni2023,
   title = {Rubric-based learner modelling via noisy gates {B}ayesian networks for computational thinking skills assessment},
   journal = {Journal of Communications Software and System},
   volume = {19},
   author = {Adorni, G. and Mangili, F. and Piatti, A. and Bonesana, C. and Antonucci, A.},
   number = {1},
   pages = {52--64},
   year = {2023},
   doi = {10.24138/jcomss-2022-0169},
   url = {}
}
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Allan, J., Mangili, F., Derboni, M., Gisler, L., Hainoun, A., Rizzoli, A., Ventriglia, L., Sulzer, M. (2023). A semantic data framework to support data-driven demand forecasting. In 2600(2), 022001.

A semantic data framework to support data-driven demand forecasting

@INPROCEEDINGS{mangili2023b,
   title = {A semantic data framework to support data-driven demand forecasting},
   journal = {Journal of Physics: Conference Series},
   volume = {2600},
   author = {Allan, J. and Mangili, F. and Derboni, M. and Gisler, L. and Hainoun, A. and Rizzoli, A. and Ventriglia, L. and Sulzer, M.},
   number = {2},
   pages = {022001},
   year = {2023},
   doi = {10.1088/1742-6596/2600/2/022001},
   url = {}
}
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Barcellona, S., Cannelli, L., Colnago, S., Laurano, C., Piegari, L. (2023). Cycle aging effect on lithium-ion battery resistance: a machine learning approach. In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 389–394.

Cycle aging effect on lithium-ion battery resistance: a machine learning approach

@INPROCEEDINGS{cannelli2023b,
   title = {Cycle aging effect on lithium-ion battery resistance: a machine learning approach},
   booktitle = {2023 {IEEE} International Conference on Metrology for {eXtended} Reality, Artificial Intelligence and Neural Engineering ({MetroXRAINE})},
   author = {Barcellona, S. and Cannelli, L. and Colnago, S. and Laurano, C. and Piegari, L.},
   pages = {389--394},
   year = {2023},
   doi = {10.1109/MetroXRAINE58569.2023.10405749},
   url = {}
}
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Benavoli, A., Azzimonti, D., Piga, D. (2023). Learning choice functions with Gaussian processes. In Evans, Robin J., Shpitser, Ilya (Eds),, Proceedings of Machine Learning Research 216, PMLR, pp. 141–151.

Learning choice functions with Gaussian processes

@INPROCEEDINGS{azzimonti2023b,
   title = {Learning choice functions with {G}aussian processes},
   editor = {Evans, Robin J. and Shpitser, Ilya},
   publisher = {PMLR},
   series = {Proceedings of Machine Learning Research},
   volume = {216},
   author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
   pages = {141--151},
   year = {2023},
   doi = {},
   url = {https://proceedings.mlr.press/v216/benavoli23a.html}
}
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Benavoli, A., Azzimonti, D., Piga, D. (2023). Bayesian optimization for choice data. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, ACM, pp. 2272–2279.

Bayesian optimization for choice data

@INPROCEEDINGS{azzimontid2023a,
   title = {Bayesian optimization for choice data},
   publisher = {ACM},
   booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},
   author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
   pages = {2272--2279},
   year = {2023},
   doi = {10.1145/3583133.3596324},
   url = {}
}
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Benavoli, A., Facchini, A., Zaffalon, M. (2023). Closure operators, classifiers and desirability. In Quaeghebeur, E., Miranda, E., Montes, I., Vantaggi, B. (Eds), ISIPTA '23: Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, PLMR 215, JMLR, pp. 25–36.

Closure operators, classifiers and desirability

@INPROCEEDINGS{benavoli2023a,
   title = {Closure operators, classifiers and desirability},
   editor = {Quaeghebeur, E. and Miranda, E. and Montes, I. and Vantaggi, B.},
   publisher = {JMLR},
   series = {PLMR},
   volume = {215},
   booktitle = {{ISIPTA} '23: Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications},
   author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
   pages = {25--36},
   year = {2023},
   doi = {},
   url = {https://proceedings.mlr.press/v215/benavoli23a.html}
}
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Bernasconi, A., Zanga, A., Lucas, P.J.F., Scutari, M., Stella, F. (2023). Towards a transportable causal network model based on observational healthcare data. In Proceedings of the 2nd Workshop on Artificial Intelligence for Healthcare, 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023).

Towards a transportable causal network model based on observational healthcare data

@INPROCEEDINGS{scutari2023c,
   title = {Towards a transportable causal network model based on observational healthcare data},
   booktitle = {Proceedings of the 2nd Workshop on Artificial Intelligence for Healthcare, 22nd International Conference of the Italian Association for Artificial Intelligence ({AIxIA} 2023)},
   author = {Bernasconi, A. and Zanga, A. and Lucas, P.J.F. and Scutari, M. and Stella, F.},
   year = {2023},
   doi = {},
   url = {https://ceur-ws.org/Vol-3578}
}
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Bregoli, A., Rathsman, K., Scutari, M., Stella, F., Mogesen, S.W. (2023). Analyzing complex systems with cascades using continuous time Bayesian networks. In Proceedings of the 30th International Symposium on Temporal Representation and Reasoning (TIME23), pp. 8:1–8:21.

Analyzing complex systems with cascades using continuous time Bayesian networks

@INPROCEEDINGS{scutari2023b,
   title = {Analyzing complex systems with cascades using continuous time {B}ayesian networks},
   booktitle = {Proceedings of the 30th International Symposium on Temporal Representation and Reasoning ({TIME23})},
   author = {Bregoli, A. and Rathsman, K. and Scutari, M. and Stella, F. and Mogesen, S.W.},
   pages = {8:1--8:21},
   year = {2023},
   doi = {},
   url = {https://paperswithcode.com/paper/analyzing-complex-systems-with-cascades-using}
}
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Briganti, G., Scutari, M., McNally, R.J. (2023). A tutorial on Bayesian networks for psychopathology researchers. Psychological Methods 28(4), pp. 947–961.

A tutorial on Bayesian networks for psychopathology researchers

@ARTICLE{scutari2023h,
   title = {A tutorial on {B}ayesian networks for psychopathology researchers},
   journal = {Psychological Methods},
   volume = {28},
   author = {Briganti, G. and Scutari, M. and McNally, R.J.},
   number = {4},
   pages = {947--961},
   year = {2023},
   doi = {doi.org/10.1037/met0000479},
   url = {}
}
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Cannelli, L., Nuti, G., Sala, M., Szehr, O. (2023). Hedging using reinforcement learning: contextual k-armed bandit versus Q-learning. The Journal of Finance and Data Science 9, 100101.

Hedging using reinforcement learning: contextual k-armed bandit versus Q-learning

@ARTICLE{szehr2023c,
   title = {Hedging using reinforcement learning: contextual k-armed bandit versus {Q}-learning},
   journal = {The Journal of Finance and Data Science},
   publisher = {Elsevier},
   volume = {9},
   author = {Cannelli, L. and Nuti, G. and Sala, M. and Szehr, O.},
   pages = {100101},
   year = {2023},
   doi = {10.1016/j.jfds.2023.100101},
   url = {}
}
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Cannelli, L., Zhu, M., Farina, F., Bemporad, A., Piga, D. (2023). Multi-agent active learning for distributed black-box optimization. IEEE Control Systems Letters 7, pp. 1488–1493.

Multi-agent active learning for distributed black-box optimization

@ARTICLE{cannelli2023a,
   title = {Multi-agent active learning for distributed black-box optimization},
   journal = {{IEEE} Control Systems Letters},
   volume = {7},
   author = {Cannelli, L. and Zhu, M. and Farina, F. and Bemporad, A. and Piga, D.},
   pages = {1488--1493},
   year = {2023},
   doi = {10.1109/LCSYS.2023.3270347},
   url = {}
}
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Casanova, A., Benavoli, A., Zaffalon, M. (2023). Nonlinear desirability as a linear classification problem. International Journal of Approximate Reasoning 152, pp. 1–32.

Nonlinear desirability as a linear classification problem

@ARTICLE{casanova2023a,
   title = {Nonlinear desirability as a linear classification problem},
   journal = {International Journal of Approximate Reasoning},
   volume = {152},
   author = {Casanova, A. and Benavoli, A. and Zaffalon, M.},
   pages = {1--32},
   year = {2023},
   doi = {10.1016/j.ijar.2022.10.008},
   url = {}
}
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Cellina, F., Simão, J.V., Mangili, F., Vermes, N., Granato, P. (2023). Sustainable mobility persuasion via smartphone apps: Lessons from a Swiss case study on how to design point-based rewarding systems. Travel Behaviour and Society 31, pp. 178–188.

Sustainable mobility persuasion via smartphone apps: Lessons from a Swiss case study on how to design point-based rewarding systems

@ARTICLE{mangili2023,
   title = {Sustainable mobility persuasion via smartphone apps: {L}essons from a {S}wiss case study on how to design point-based rewarding systems},
   journal = {Travel Behaviour and Society},
   volume = {31},
   author = {Cellina, F. and Sim\~ao, J.V. and Mangili, F. and Vermes, N. and Granato, P.},
   pages = {178--188},
   year = {2023},
   doi = {10.1016/j.tbs.2022.12.001},
   url = {}
}
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Corani, G., Azzimonti, D., Rubattu, N. (2023). Probabilistic reconciliation of count time series. International Journal of Forecasting.

Probabilistic reconciliation of count time series

@ARTICLE{CORANI2023a,
   title = {Probabilistic reconciliation of count time series},
   journal = {International Journal of Forecasting},
   author = {Corani, G. and Azzimonti, D. and Rubattu, N.},
   year = {2023},
   doi = {10.1016/j.ijforecast.2023.04.003},
   url = {}
}
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Corradini, F., Flammini, F., Antonucci, A. (2023). Probabilistic modelling for trustworthy artificial intelligence in drone-supported autonomous wheelchairs. In Proceedings of the First International Symposium on Trustworthy Autonomous Systems, Association for Computing Machinery.

Probabilistic modelling for trustworthy artificial intelligence in drone-supported autonomous wheelchairs

@INPROCEEDINGS{corradini2023a,
   title = {Probabilistic modelling for trustworthy artificial intelligence in drone-supported autonomous wheelchairs},
   publisher = {Association for Computing Machinery},
   booktitle = {Proceedings of the First International Symposium on Trustworthy Autonomous Systems},
   author = {Corradini, F. and Flammini, F. and Antonucci, A.},
   year = {2023},
   doi = {10.1145/3597512.3599716},
   url = {}
}
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Gupta, A., Mejari, M., Falcone, P., Piga, D. (2023). Computation of parameter dependent robust invariant sets for lpv models with guaranteed performance. Automatica 151, 110920.

Computation of parameter dependent robust invariant sets for lpv models with guaranteed performance

@ARTICLE{mejari2023a,
   title = {Computation of parameter dependent robust invariant sets for lpv models with guaranteed performance},
   journal = {Automatica},
   volume = {151},
   author = {Gupta, A. and Mejari, M. and Falcone, P. and Piga, D.},
   pages = {110920},
   year = {2023},
   doi = {10.1016/j.automatica.2023.110920},
   url = {https://www.sciencedirect.com/science/article/pii/S0005109823000705}
}
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Huber, D., Chen, Y., Antonucci, A., Darwiche, A., Zaffalon, M. (2023). Tractable bounding of counterfactual queries by knowledge compilation. In The 6th Workshop on Tractable Probabilistic Modeling.

Tractable bounding of counterfactual queries by knowledge compilation

@INPROCEEDINGS{huber2023a,
   title = {Tractable bounding of counterfactual queries by knowledge compilation},
   booktitle = {The 6th Workshop on Tractable Probabilistic Modeling},
   author = {Huber, D. and Chen, Y. and Antonucci, A. and Darwiche, A. and Zaffalon, M.},
   year = {2023},
   doi = {},
   url = {https://openreview.net/forum?id=kPb6CQZo93}
}
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Kanjirangat, V., Antonucci, A. (2023). Edge labelling in narrative knowledge graphs. In Proceedings of Text2Story — Sixth Workshop on Narrative Extraction From Texts held in conjunction with the 45th European Conference on Information Retrieval (ECIR 2023) 3370, CEUR Workshop Proceedings, pp. 135–142.

Edge labelling in narrative knowledge graphs

@INPROCEEDINGS{kanjirangat2023a,
   title = {Edge labelling in narrative knowledge graphs},
   publisher = {CEUR Workshop Proceedings},
   volume = {3370},
   booktitle = {Proceedings of {Text2Story} — Sixth Workshop on Narrative Extraction From Texts {h}eld in {c}onjunction {w}ith the 45th European Conference on Information Retrieval ({ECIR} 2023)},
   author = {Kanjirangat, V. and Antonucci, A.},
   pages = {135--142},
   year = {2023},
   doi = {},
   url = {https://ceur-ws.org/Vol-3370/}
}
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Liew, B.X.W., Palacios-Ceña, M., Scutari, M., Fuensalida-Novo, S., Guerrero-Peral, A., Ordás-Bandera, C., Pareja, J.A., & Fernández-de-Las-Peñas, C. (2023). Path analysis models integrating psychological, psycho-physical and clinical variables in individuals with tension-type headache. The Journal of Pain 24(3), pp. 426–436.

Path analysis models integrating psychological, psycho-physical and clinical variables in individuals with tension-type headache

@ARTICLE{scutari2023i,
   title = {Path analysis models integrating psychological, psycho-physical and clinical variables in individuals with tension-type headache},
   journal = {The Journal of Pain},
   volume = {24},
   author = {Liew, B.X.W. and Palacios-Ce\~na, M. and Scutari, M. and Fuensalida-Novo, S. and Guerrero-Peral, A. and Ord\'as-Bandera, C. and Pareja, J.A. and & Fern\'andez-de-Las-Pe\~nas, C.},
   number = {3},
   pages = {426--436},
   year = {2023},
   doi = {10.1016/j.jpain.2022.10.003},
   url = {}
}
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Liew, B.X.W., Hartvigsen, J., Scutari, M., Kongsted, A. (2023). Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study. Journal of Clinical Epidemiology 153, pp. 66–77.

Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study

@ARTICLE{scutari2023f,
   title = {Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional {GLA}:{D} {B}ack study},
   journal = {Journal of Clinical Epidemiology},
   publisher = {Elsevier},
   volume = {153},
   author = {Liew, B.X.W. and Hartvigsen, J. and Scutari, M. and Kongsted, A.},
   pages = {66--77},
   year = {2023},
   doi = {10.1016/j.jclinepi.2022.11.010 },
   url = {}
}
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Maggiolo, M., Szehr, O. (2023). Overfitting in portfolio optimization. Journal of Risk Model Validation 17(3), pp. 1–33.

Overfitting in portfolio optimization

@ARTICLE{szehr2023a,
   title = {Overfitting in portfolio optimization},
   journal = {Journal of Risk Model Validation},
   volume = {17},
   author = {Maggiolo, M. and Szehr, O.},
   number = {3},
   pages = {1--33},
   year = {2023},
   doi = {10.21314/JRMV.2023.005},
   url = {}
}
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Mejari, M., Forgione, M., Piga, D. (2023). Variational autoencoder for the identification of piecewise models. In 56(2), pp. 4055–4060.

Variational autoencoder for the identification of piecewise models

@INPROCEEDINGS{mejari2023c,
   title = {Variational autoencoder for the identification of piecewise models},
   journal = {{IFAC}-{PapersOnLine}},
   volume = {56},
   author = {Mejari, M. and Forgione, M. and Piga, D.},
   number = {2},
   pages = {4055--4060},
   year = {2023},
   doi = {10.1016/j.ifacol.2023.10.1728},
   url = {}
}
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Mejari, M., Gupta, A., Piga, D. (2023). Data-driven computation of robust invariant sets and gain-scheduled controllers for linear parameter-varying systems. IEEE Control Systems Letters 7, pp. 3355–3360.

Data-driven computation of robust invariant sets and gain-scheduled controllers for linear parameter-varying systems

@ARTICLE{mejari2023d,
   title = {Data-driven computation of robust invariant sets and gain-scheduled controllers for linear parameter-varying systems},
   journal = {{IEEE} Control Systems Letters},
   volume = {7},
   author = {Mejari, M. and Gupta, A. and Piga, D.},
   pages = {3355--3360},
   year = {2023},
   doi = {10.1109/LCSYS.2023.3329291},
   url = {}
}
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Mejari, M., Piga, D. (2023). Direct identification of continuous-time linear switched state-space models. In 56(2), pp. 4210–4215.

Direct identification of continuous-time linear switched state-space models

@INPROCEEDINGS{mejari2023b,
   title = {Direct identification of continuous-time linear switched state-space models},
   journal = {{IFAC}-{PapersOnLine}},
   volume = {56},
   author = {Mejari, M. and Piga, D.},
   number = {2},
   pages = {4210--4215},
   year = {2023},
   doi = {10.1016/j.ifacol.2023.10.1773},
   url = {}
}
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Miranda, E., Zaffalon, M. (2023). Nonlinear desirability theory. International Journal of Approximate Reasoning 154, pp. 176–199.

Nonlinear desirability theory

@ARTICLE{miranda2023a,
   title = {Nonlinear desirability theory},
   journal = {International Journal of Approximate Reasoning},
   volume = {154},
   author = {Miranda, E. and Zaffalon, M.},
   pages = {176--199},
   year = {2023},
   doi = {10.1016/j.ijar.2022.12.015},
   url = {}
}
Download
Mitrović, S., Frisone, F., Gupta, S., Lucifora, C., Čarapić, D., Schillaci, C., Di Giovanni, S., Singh, A. (2023). Annotating panic in social media using active learning, transformers and domain knowledge. 2023 IEEE International Conference on Data Mining Workshops (ICDMW).

Annotating panic in social media using active learning, transformers and domain knowledge

@ARTICLE{mitrovic2023a,
   title = {Annotating panic in social media using active learning, transformers and domain knowledge},
   journal = {2023 {IEEE} International Conference on Data Mining Workshops ({ICDMW})},
   publisher = {IEEE},
   author = {Mitrović, S. and Frisone, F. and Gupta, S. and Lucifora, C. and Čarapić, D. and Schillaci, C. and Di Giovanni, S. and Singh, A.},
   year = {2023},
   doi = {},
   url = {http://xhealth.one/dmmd}
}
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Rubattu, N., Maroni, G., Corani, G. (2023). Electricity load and peak forecasting: feature engineering, probabilistic LightGBM and temporal hierarchies. In Ifrim, G., Tavenard, R., Bagnall, A., Schaefer, P., Malinowski, S., Guyet, T., Lemaire, V. (Eds), Advanced Analytics and Learning on Temporal Data, Springer Nature Switzerland, pp. 276–292.

Electricity load and peak forecasting: feature engineering, probabilistic LightGBM and temporal hierarchies

@INPROCEEDINGS{corani2023b,
   title = {Electricity load and peak forecasting: feature engineering, probabilistic {LightGBM} and temporal hierarchies},
   editor = {Ifrim, G. and Tavenard, R. and Bagnall, A. and Schaefer, P. and Malinowski, S. and Guyet, T. and Lemaire, V.},
   publisher = {Springer Nature Switzerland},
   booktitle = {Advanced Analytics and Learning on Temporal Data},
   author = {Rubattu, N. and Maroni, G. and Corani, G.},
   pages = {276--292},
   year = {2023},
   doi = {10.1007/978-3-031-49896-1_18},
   url = {}
}
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Schürch, M., Azzimonti, D., Benavoli, A., Zaffalon, M. (2023). Correlated product of experts for sparse Gaussian process regression. Machine Learning 112, pp. 1411–1432.

Correlated product of experts for sparse Gaussian process regression

@ARTICLE{schurch2023,
   title = {Correlated product of experts for sparse {G}aussian process regression},
   journal = {Machine Learning},
   publisher = {Springer},
   volume = {112},
   author = {Sch\"urch, M. and Azzimonti, D. and Benavoli, A. and Zaffalon, M.},
   pages = {1411--1432},
   year = {2023},
   doi = {10.1007/s10994-022-06297-3},
   url = {}
}
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Scutari, M., Malvestio, M. (2023). Developing and running machine learning software: machine learning operations (MLOps). Wiley StatsRef: Statistics Reference Online, pp. 1–8.

Developing and running machine learning software: machine learning operations (MLOps)

@ARTICLE{scutari2023e,
   title = {Developing and running machine learning software: machine learning operations ({MLOps})},
   journal = {Wiley {StatsRef}: Statistics Reference Online},
   publisher = {John Wiley & Sons, Ltd},
   author = {Scutari, M. and Malvestio, M.},
   pages = {1--8},
   year = {2023},
   doi = {10.1002/9781118445112.stat08455},
   url = {}
}
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Scutari, M., Malvestio, M. (2023). Machine learning software and pipelines. Wiley StatsRef: Statistics Reference Online, pp. 1–6.

Machine learning software and pipelines

@ARTICLE{scutari2023g,
   title = {Machine learning software and pipelines},
   journal = {Wiley {StatsRef}: Statistics Reference Online},
   author = {Scutari, M. and Malvestio, M.},
   pages = {1--6},
   year = {2023},
   doi = {10.1002/9781118445112.stat08454},
   url = {}
}
Download
Selmonaj, A., Szehr, O., Del Rio, G., Antonucci, A., Schneider, A., Rüegsegger, M. (2023). Hierarchical multi-agent reinforcement learning for air combat maneuvering. In , IEEE.

Hierarchical multi-agent reinforcement learning for air combat maneuvering

@INPROCEEDINGS{selmonaj2023a,
   title = {Hierarchical multi-agent reinforcement learning for air combat maneuvering},
   journal = {Proceedings of the 22nd International on Machine Learning and Applications},
   publisher = {IEEE},
   author = {Selmonaj, A. and Szehr, O. and Del Rio, G. and Antonucci, A. and Schneider, A. and R\"uegsegger, M.},
   year = {2023},
   doi = {},
   url = {https://www.icmla-conference.org/icmla23/}
}
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Szehr, O., Maggiolo, M. (2023). Hedging of financial derivative contracts via Monte Carlo tree search. Journal of Computational Finance 27(2), pp. 47–80.

Hedging of financial derivative contracts via Monte Carlo tree search

@ARTICLE{szehr2023b,
   title = {Hedging of financial derivative contracts via {M}onte {C}arlo tree search},
   journal = {Journal of Computational Finance},
   volume = {27},
   author = {Szehr, O. and Maggiolo, M.},
   number = {2},
   pages = {47--80},
   year = {2023},
   doi = {10.21314/JCF.2023.009},
   url = {}
}
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Termine, A., Antonucci, A., Facchini, A. (2023). Machine learning explanations by surrogate causal models (MaLESCaMo). In Longo, L. (Ed), Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the First World Conference on eXplainable Artificial Intelligence (xAI-2023) 3554, CEUR Workshop Proceedings, pp. 59–64.

Machine learning explanations by surrogate causal models (MaLESCaMo)

@INPROCEEDINGS{termine2023a,
   title = {Machine learning explanations by surrogate causal models ({MaLESCaMo})},
   editor = {Longo, L.},
   publisher = {CEUR Workshop Proceedings},
   volume = {3554},
   booktitle = {Joint Proceedings of the {xAI}-2023 Late-{b}reaking Work, Demos and Doctoral Consortium {c}o-{l}ocated {w}ith the First World Conference on {eXplainable} Artificial Intelligence ({xAI}-2023)},
   author = {Termine, A. and Antonucci, A. and Facchini, A.},
   pages = {59--64},
   year = {2023},
   doi = {},
   url = {https://ceur-ws.org/Vol-3554}
}
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Termine, A., Antonucci, A., Primiero, G., Facchini, A. (2023). Imprecise probabilistic model checking for stochastic multi-agent systems. SN Computer Science 4(443).

Imprecise probabilistic model checking for stochastic multi-agent systems

@ARTICLE{termine2023b,
   title = {Imprecise probabilistic model checking for stochastic multi-agent systems},
   journal = {{SN} Computer Science},
   volume = {4},
   author = {Termine, A. and Antonucci, A. and Primiero, G. and Facchini, A.},
   number = {443},
   year = {2023},
   doi = {10.1007/s42979-023-01817-x},
   url = {}
}
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Zaffalon, M., Antonucci, A., Cabañas, R., Huber, D. (2023). Approximating counterfactual bounds while fusing observational, biased and randomised data sources. International Journal of Approximate Reasoning 162, 109023.

Approximating counterfactual bounds while fusing observational, biased and randomised data sources

@ARTICLE{zaffalon2023a,
   title = {Approximating counterfactual bounds while fusing observational, biased and randomised data sources},
   journal = {International Journal of Approximate Reasoning},
   volume = {162},
   author = {Zaffalon, M. and Antonucci, A. and Caba\~nas, R. and Huber, D.},
   pages = {109023},
   year = {2023},
   doi = {10.1016/j.ijar.2023.109023},
   url = {}
}
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Zanga, A., Bernasconi, A., Lucas, P.J.F., Pijnenborg, H., Rejinen, C., Scutari, M., Stella, F. (2023). Causal discovery with missing data in a multicentric clinical study. In Juarez, Jose M., Marcos, Mar, Stiglic, Gregor, Tucker, Allan (Eds), Proceedings of the 21st International Conference on Artificial Intelligence in Medicine (AIME23), Springer Nature Switzerland, pp. 40–44.

Causal discovery with missing data in a multicentric clinical study

@INPROCEEDINGS{scutari2023d,
   title = {Causal discovery with missing data in a multicentric clinical study},
   editor = {Juarez, Jose M. and Marcos, Mar and Stiglic, Gregor and Tucker, Allan},
   publisher = {Springer Nature Switzerland},
   booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence in Medicine ({AIME23})},
   author = {Zanga, A. and Bernasconi, A. and Lucas, P.J.F. and Pijnenborg, H. and Rejinen, C. and Scutari, M. and Stella, F.},
   pages = {40--44},
   year = {2023},
   doi = {10.1007/978-3-031-34344-5_5},
   url = {}
}
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2022

Allante, L., Korfiati, A., Androutsos, L., Stojceski, F., Bompotas, A., Giannikos, I., Raftopoulos, C., Malavolta, M., Grasso, G., Mavroudi, S., Theofilatos, K., Piga, D., Deriu, M. (2022). Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. Scientific Reports, Nature Publishing Group 12(1), 21735.

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

@ARTICLE{piga2022a,
   title = {Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach},
   journal = {Scientific Reports, Nature Publishing Group},
   volume = {12},
   author = {Allante, L. and Korfiati, A. and Androutsos, L. and Stojceski, F. and Bompotas, A. and Giannikos, I. and Raftopoulos, C. and Malavolta, M. and Grasso, G. and Mavroudi, S. and Theofilatos, K. and Piga, D. and Deriu, M.},
   number = {1},
   pages = {21735},
   year = {2022},
   doi = {10.1038/s41598-022-25935-3},
   url = {}
}
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Antonucci, A., Mangili, F., Bonesana, C., Adorni, G. (2022). Intelligent tutoring systems by Bayesian nets with noisy gates. In 35.

Intelligent tutoring systems by Bayesian nets with noisy gates

@INPROCEEDINGS{antonucci2022a,
   title = {Intelligent tutoring systems by {B}ayesian nets with noisy gates},
   journal = {The International {FLAIRS} Conference Proceedings},
   volume = {35},
   author = {Antonucci, A. and Mangili, F. and Bonesana, C. and Adorni, G.},
   year = {2022},
   doi = {10.32473/flairs.v35i.130692},
   url = {}
}
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Azzimonti, L., Corani, G., Scutari, M. (2022). A bayesian hierarchical score for structure learning from related data sets. International Journal of Approximate Reasoning 142, pp. 248–265.

A bayesian hierarchical score for structure learning from related data sets

@ARTICLE{azzimonti2022a,
   title = {A bayesian hierarchical score for structure learning from related data sets},
   journal = {International Journal of Approximate Reasoning},
   volume = {142},
   author = {Azzimonti, L. and Corani, G. and Scutari, M.},
   pages = {248--265},
   year = {2022},
   doi = {10.1016/j.ijar.2021.11.013},
   url = {}
}
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Benavoli, A., Facchini, A., Zaffalon, M. (2022). Quantum indistinguishability through exchangeability. International Journal of Approximate Reasoning 151, pp. 389–412.

Quantum indistinguishability through exchangeability

@ARTICLE{benavoli2022a,
   title = {Quantum indistinguishability through exchangeability},
   journal = {International Journal of Approximate Reasoning},
   volume = {151},
   author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
   pages = {389--412},
   year = {2022},
   doi = {10.1016/j.ijar.2022.10.003},
   url = {}
}
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Bianchi, F., Piroddi, L., Bemporad, A., Halasz, G., Villani, M., Piga, D. (2022). Active preference-based optimization for human-in-the-loop feature selection. European Journal of Control 66, 100647.

Active preference-based optimization for human-in-the-loop feature selection

@ARTICLE{piga2022c,
   title = {Active preference-based optimization for human-in-the-loop feature selection},
   journal = {European Journal of Control},
   volume = {66},
   author = {Bianchi, F. and Piroddi, L. and Bemporad, A. and Halasz, G. and Villani, M. and Piga, D.},
   pages = {100647},
   year = {2022},
   doi = {10.1016/j.ejcon.2022.100647},
   url = {}
}
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Casanova, A., Kohlas, J., Zaffalon, M. (2022). Information algebras in the theory of imprecise probabilities. International Journal of Approximate Reasoning 142, pp. 383–416.

Information algebras in the theory of imprecise probabilities

@ARTICLE{casanova2022a,
   title = {Information algebras in the theory of imprecise probabilities},
   journal = {International Journal of Approximate Reasoning},
   volume = {142},
   author = {Casanova, A. and Kohlas, J. and Zaffalon, M.},
   pages = {383--416},
   year = {2022},
   doi = {10.1016/j.ijar.2021.12.017},
   url = {}
}
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Casanova, A., Kohlas, J., Zaffalon, M. (2022). Information algebras in the theory of imprecise probabilities, an extension. International Journal of Approximate Reasoning 150, pp. 311–336.

Information algebras in the theory of imprecise probabilities, an extension

@ARTICLE{casanova2022b,
   title = {Information algebras in the theory of imprecise probabilities, an extension},
   journal = {International Journal of Approximate Reasoning},
   volume = {150},
   author = {Casanova, A. and Kohlas, J. and Zaffalon, M.},
   pages = {311--336},
   year = {2022},
   doi = {10.1016/j.ijar.2022.09.003},
   url = {}
}
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Delfanti, G., Cortesi, F., Perini, A., Antonini, G., Azzimonti, L., de Lalla, C., Garavaglia, C., Squadrito, M.L., Fedeli, M., Consonni, M., Sesana, S., Re, F., Shen, H., Dellabona, P., Casorati, G. (2022). TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells. Science Immunology 7(74), eabn6563.

TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells

@ARTICLE{azzimonti2022b,
   title = {{TCR}-engineered {iNKT} cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells},
   journal = {Science Immunology},
   volume = {7},
   author = {Delfanti, G. and Cortesi, F. and Perini, A. and Antonini, G. and Azzimonti, L. and de Lalla, C. and Garavaglia, C. and Squadrito, M.L. and Fedeli, M. and Consonni, M. and Sesana, S. and Re, F. and Shen, H. and Dellabona, P. and Casorati, G.},
   number = {74},
   pages = {eabn6563},
   year = {2022},
   doi = {10.1126/sciimmunol.abn6563},
   url = {}
}
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Delucchi, M., Spinner, G.R., Scutari, M., Bijlenga, P., Morel, S., Friedrich, C.M., Furrer, R., Hirsch, S. (2022). Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. Computers in Biology and Medicine 147, 105740.

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

@ARTICLE{scutari22c,
   title = {Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors},
   journal = {Computers in Biology and Medicine},
   volume = {147},
   author = {Delucchi, M. and Spinner, G.R. and Scutari, M. and Bijlenga, P. and Morel, S. and Friedrich, C.M. and Furrer, R. and Hirsch, S.},
   pages = {105740},
   year = {2022},
   doi = {10.1016/j.compbiomed.2022.105740},
   url = {}
}
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Držajić, D., Wiessner, M., Maradia, U., Piga, D. (2022). Virtual operators with self and transfer learning ability in EDM. Procedia CIRP 113, pp. 17–22.

Virtual operators with self and transfer learning ability in EDM

@ARTICLE{piga2022e,
   title = {Virtual operators with self and transfer learning ability in {EDM}},
   journal = {Procedia {CIRP}},
   volume = {113},
   author = {Držajić, D. and Wiessner, M. and Maradia, U. and Piga, D.},
   pages = {17--22},
   year = {2022},
   doi = {10.1016/j.procir.2022.09.113},
   url = {}
}
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Formenti, A., Bucca, G., Shahid, A.A., Piga, D., Roveda, L. (2022). Improved Impedance/Admittance switching controller for the interaction with a variable stiffness environment. Complex Engineering Systems 2(3), 12.

Improved Impedance/Admittance switching controller for the interaction with a variable stiffness environment

@ARTICLE{Roveda2022f,
   title = {Improved {Impedance/Admittance} switching controller for the interaction with a variable stiffness environment},
   journal = {Complex Engineering Systems},
   volume = {2},
   author = {Formenti, A. and Bucca, G. and Shahid, A.A. and Piga, D. and Roveda, L.},
   number = {3},
   pages = {12},
   year = {2022},
   doi = {10.20517/ces.2022.16},
   url = {}
}
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Hiel, S., Nicolaers, L., Ortega Vazquez, C., Mitrović, S., Baesens, B., De Weerdt, J. (2022). Evaluation of joint modeling techniques for node embedding and community detection on graphs. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

Evaluation of joint modeling techniques for node embedding and community detection on graphs

@INPROCEEDINGS{sandra2022a,
   title = {Evaluation of joint modeling techniques for node embedding and community detection on graphs},
   booktitle = {2022 {IEEE/ACM} International Conference on Advances in Social Networks Analysis and Mining ({ASONAM})},
   author = {Hiel, S. and Nicolaers, L. and Ortega Vazquez, C. and Mitrović, S. and Baesens, B. and De Weerdt, J.},
   year = {2022},
   doi = {},
   url = {}
}
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Kronauer, S., Mavkov, B., Mejari, M., Piga, D., Jaques, F., d'Amario, R., Di Campli, R., Nasciuti, A. (2022). Data-driven statistical analysis for discharge position prediction on Wire EDM. Procedia CIRP 113, pp. 143–148.

Data-driven statistical analysis for discharge position prediction on Wire EDM

@ARTICLE{mejari2022b,
   title = {Data-driven statistical analysis for discharge position prediction on {W}ire {EDM}},
   journal = {Procedia {CIRP}},
   volume = {113},
   author = {Kronauer, S. and Mavkov, B. and Mejari, M. and Piga, D. and Jaques, F. and d'Amario, R. and Di Campli, R. and Nasciuti, A.},
   pages = {143--148},
   year = {2022},
   doi = {10.1016/j.procir.2022.09.122},
   url = {}
}
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Liew, B.X.W., de-la-Llave-Rincón, A.I., Scutari, M., Arias-Buría, J.L., Cook, C.E., Cleland, J., Fernández-de-las-Peñas, C. (2022). Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A Bayesian network analysis of a randomized clinical trial . Physical Therapy 102(4), pzac015.

Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A Bayesian network analysis of a randomized clinical trial

@ARTICLE{scutari22b,
   title = {Do short-term effects predict long-term improvements in women who receive manual therapy or surgery for carpal tunnel syndrome? A {B}ayesian network analysis of a randomized clinical trial },
   journal = {Physical Therapy},
   volume = {102},
   author = {Liew, B.X.W. and de-la-Llave-Rinc\'on, A.I. and Scutari, M. and Arias-Bur\'ia, J.L. and Cook, C.E. and Cleland, J. and Fern\'andez-de-las-Pe\~nas, C.},
   number = {4},
   pages = {pzac015},
   year = {2022},
   doi = {10.1093/ptj/pzac015},
   url = {}
}
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Malavolta, M., Pallante, L., Mavkov, B., Stojceski, F., Grasso, G., Korfiati, A., Mavroudi, S., Kalogeras, A., Alexakos, C., Martos, V., Daria, A., Giacomo, D.B.P.D., Theofilatos, K., Deriu, M. (2022). A survey on computational taste predictors. European Food Research and Technology 248(9), pp. 2215–2235.

A survey on computational taste predictors

@ARTICLE{piga2022d,
   title = {A survey on computational taste predictors},
   journal = {European Food Research and Technology},
   volume = {248},
   author = {Malavolta, M. and Pallante, L. and Mavkov, B. and Stojceski, F. and Grasso, G. and Korfiati, A. and Mavroudi, S. and Kalogeras, A. and Alexakos, C. and Martos, V. and Daria, A. and Giacomo, D.B.P.D. and Theofilatos, K. and Deriu, M.},
   number = {9},
   pages = {2215--2235},
   year = {2022},
   doi = {10.1007/s00217-022-04044-5},
   url = {}
}
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Malpetti, D., Bellisario, A., Macchiavello, C. (2022). Multipartite entanglement in qudit hypergraph states. Journal of Physics A: Mathematical and Theoretical 55(41), 415301.

Multipartite entanglement in qudit hypergraph states

@ARTICLE{malpetti2022b,
   title = {Multipartite entanglement in qudit hypergraph states},
   journal = {Journal of Physics A: Mathematical and Theoretical},
   editor = {IOP Publishing},
   volume = {55},
   author = {Malpetti, D. and Bellisario, A. and Macchiavello, C.},
   number = {41},
   pages = {415301},
   year = {2022},
   doi = {10.1088/1751-8121/ac91b2},
   url = {}
}
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Mangili, F., Adorni, G., Piatti, A., Bonesana, C., Antonucci, A. (2022). Modelling assessment rubrics through Bayesian networks: a pragmatic approach. Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

Modelling assessment rubrics through Bayesian networks: a pragmatic approach

@ARTICLE{mangili2022a,
   title = {Modelling assessment rubrics through {B}ayesian networks: a pragmatic approach},
   journal = {Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks ({SoftCOM})},
   publisher = {IEEE},
   author = {Mangili, F. and Adorni, G. and Piatti, A. and Bonesana, C. and Antonucci, A.},
   year = {2022},
   doi = {},
   url = {}
}
Download
Maroni, G., Pallante, L., Di Benedetto, G., Deriu, M.A., Piga, D., Grasso, G. (2022). Informed classification of sweeteners/bitterants compounds via explainable machine learning. Current Research in Food Science 5, pp. 2270–2280.

Informed classification of sweeteners/bitterants compounds via explainable machine learning

@ARTICLE{maroni2022a,
   title = {Informed classification of sweeteners/bitterants compounds via explainable machine learning},
   journal = {Current Research in Food Science},
   volume = {5},
   author = {Maroni, G. and Pallante, L. and Di Benedetto, G. and Deriu, M.A. and Piga, D. and Grasso, G.},
   pages = {2270--2280},
   year = {2022},
   doi = {10.1016/j.crfs.2022.11.014},
   url = {}
}
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Mejari, M., Mavkov, B., Forgione, M., Piga, D. (2022). Direct identification of continuous-time lpv state-space models via an integral architecture. Automatica 142, 110407.

Direct identification of continuous-time lpv state-space models via an integral architecture

@ARTICLE{mejari2022c,
   title = {Direct identification of continuous-time lpv state-space models via an integral architecture},
   journal = {Automatica},
   volume = {142},
   author = {Mejari, M. and Mavkov, B. and Forgione, M. and Piga, D.},
   pages = {110407},
   year = {2022},
   doi = {https://doi.org/10.1016/j.automatica.2022.110407},
   url = {https://www.sciencedirect.com/science/article/pii/S0005109822002606}
}
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Mejari, M., Piga, D. (2022). Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling. ASME Letters in Dynamic Systems and Control 2(1).

Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling

@ARTICLE{mejari2022a,
   title = {Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling},
   journal = {{ASME} Letters in Dynamic Systems and Control},
   volume = {2},
   author = {Mejari, M. and Piga, D.},
   number = {1},
   year = {2022},
   doi = {10.1115/1.4051491},
   url = {}
}
Download
Mitrović, S., Kanjirangat, V. (2022). Enhancing BERT performance with contextual valence shifters for panic detection in COVID-19 tweets. In The 6th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2022).

Enhancing BERT performance with contextual valence shifters for panic detection in COVID-19 tweets

@INPROCEEDINGS{mitrovic2022b,
   title = {Enhancing {BERT} performance with contextual valence shifters for panic detection in {COVID}-19 tweets},
   edition = {To appear},
   booktitle = {The 6th International Conference on Natural Language Processing and Information Retrieval ({NLPIR} 2022)},
   author = {Mitrović, S. and Kanjirangat, V.},
   year = {2022},
   doi = {},
   url = {}
}
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Paolillo, A., Nava, M., Piga, D., Giusti, A. (2022). Visual servoing with geometrically interpretable neural perception. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5300–5306.

Visual servoing with geometrically interpretable neural perception

@INPROCEEDINGS{piga2022b,
   title = {Visual servoing with geometrically interpretable neural perception},
   booktitle = {2022 {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
   author = {Paolillo, A. and Nava, M. and Piga, D. and Giusti, A.},
   pages = {5300--5306},
   year = {2022},
   doi = {10.1109/IROS47612.2022.9982163},
   url = {}
}
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Pesenti, M., Gandolla, M., Folcio, C., Ouyang, S., Rovelli, L., Covarrubias, M., Roveda, L. (2022). Sensor-based task ergonomics feedback for a passive low-back exoskeleton. In Miesenberger, K., Kouroupetroglou, G., Mavrou, K., Manduchi, R., Covarrubias Rodriguez, M., Penáz, P. (Eds), Computers Helping People with Special Needs, Springer International Publishing, pp. 403–410.

Sensor-based task ergonomics feedback for a passive low-back exoskeleton

@INPROCEEDINGS{Roveda2022h,
   title = {Sensor-based task ergonomics feedback for a passive low-back exoskeleton},
   editor = {Miesenberger, K. and Kouroupetroglou, G. and Mavrou, K. and Manduchi, R. and Covarrubias Rodriguez, M. and Pen\'az, P.},
   publisher = {Springer International Publishing},
   booktitle = {Computers Helping People {w}ith Special Needs},
   author = {Pesenti, M. and Gandolla, M. and Folcio, C. and Ouyang, S. and Rovelli, L. and Covarrubias, M. and Roveda, L.},
   pages = {403--410},
   year = {2022},
   doi = {10.1007/978-3-031-08645-8_47},
   url = {}
}
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Pesenti, M., Gandolla, M., Pedrocchi, A., Roveda, L. (2022). A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study. In 2022 International Conference on Rehabilitation Robotics (ICORR), pp. 1–6.

A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study

@INPROCEEDINGS{Roveda2022j,
   title = {A backbone-tracking passive exoskeleton to reduce the stress on the low-back: proof of concept study},
   booktitle = {2022 International Conference on Rehabilitation Robotics ({ICORR})},
   author = {Pesenti, M. and Gandolla, M. and Pedrocchi, A. and Roveda, L.},
   pages = {1--6},
   year = {2022},
   doi = {10.1109/ICORR55369.2022.9896514},
   url = {}
}
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Pozzi, L., Gandolla, M., Pura, F., Maccarini, M., Pedrocchi, A., Braghin, F., Piga, D., Roveda, L. (2022). Grasping learning, optimization, and knowledge transfer in the robotics field. Scientific Reports 12(1), 4481.

Grasping learning, optimization, and knowledge transfer in the robotics field

@ARTICLE{Roveda2022c,
   title = {Grasping learning, optimization, and knowledge transfer in the robotics field},
   journal = {Scientific Reports},
   volume = {12},
   author = {Pozzi, L. and Gandolla, M. and Pura, F. and Maccarini, M. and Pedrocchi, A. and Braghin, F. and Piga, D. and Roveda, L.},
   number = {1},
   pages = {4481},
   year = {2022},
   doi = {10.1038/s41598-022-08276-z},
   url = {}
}
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Pozzi, L., Gandolla, M., Roveda, L. (2022). Pointing Gestures for Human-Robot Interaction in Service Robotics: a feasibility study. In Computers Helping People with Special Needs, Springer International Publishing, pp. 461–468.

Pointing Gestures for Human-Robot Interaction in Service Robotics: a feasibility study

@INPROCEEDINGS{Rovedag,
   title = {Pointing {G}estures for {H}uman-{R}obot {I}nteraction in {S}ervice {R}obotics: a feasibility study},
   publisher = {Springer International Publishing},
   booktitle = {Computers Helping People {w}ith Special Needs},
   author = {Pozzi, L. and Gandolla, M. and Roveda, L.},
   pages = {461--468},
   year = {2022},
   doi = {10.1007/978-3-031-08645-8 54},
   url = {}
}
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Ravasi, D., Mangili, F., Huber, D., Azzimonti, L., Engeler, L., Vermes, N., Del Rio, G., Guidi, V., Tonolla, M., Flacio, E. (2022). Risk-based mapping tools for surveillance and control of the invasive mosquito Aedes albopictus in Switzerland. International Journal of Environmental Research and Public Health 19(6), 3220.

Risk-based mapping tools for surveillance and control of the invasive mosquito Aedes albopictus in Switzerland

@ARTICLE{mangili2022b,
   title = {Risk-based mapping tools for surveillance and control of the invasive mosquito {A}edes albopictus in {S}witzerland},
   journal = {International Journal of Environmental Research and Public Health},
   volume = {19},
   author = {Ravasi, D. and Mangili, F. and Huber, D. and Azzimonti, L. and Engeler, L. and Vermes, N. and Del Rio, G. and Guidi, V. and Tonolla, M. and Flacio, E.},
   number = {6},
   pages = {3220},
   year = {2022},
   doi = {10.3390/ijerph19063220},
   url = {}
}
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Ravasi, D., Mangili, F., Huber, D., Cannata, M., Strigaro, D., Flacio, E. (2022). The effects of microclimatic winter conditions in urban areas on the risk of establishment for Aedes albopictus. Scientific Reports 12(1), pp. 1–14.

The effects of microclimatic winter conditions in urban areas on the risk of establishment for Aedes albopictus

@ARTICLE{mangili2022c,
   title = {The effects of microclimatic winter conditions in urban areas on the risk of establishment for {A}edes albopictus},
   journal = {Scientific Reports},
   publisher = {Nature Publishing Group},
   volume = {12},
   author = {Ravasi, D. and Mangili, F. and Huber, D. and Cannata, M. and Strigaro, D. and Flacio, E.},
   number = {1},
   pages = {1--14},
   year = {2022},
   doi = {10.1038/s41598-022-20436-9},
   url = {}
}
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Rinaldi, A., Lazareth, H., Poindessous, V., Nemazanyy, I., Sampaio, J.L., Malpetti, D., Bignon, Y., Naesens, M., Rabant, M., Anglicheau, D., Cippà, P.E., Pallet, N. (2022). Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury. JCI Insight 7(18).

Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury

@ARTICLE{malpetti2022a,
   title = {Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury},
   journal = {{JCI} Insight},
   editor = {The American Society for Clinical Investigation},
   volume = {7},
   author = {Rinaldi, A. and Lazareth, H. and Poindessous, V. and Nemazanyy, I. and Sampaio, J.L. and Malpetti, D. and Bignon, Y. and Naesens, M. and Rabant, M. and Anglicheau, D. and Cipp\`a, P.E. and Pallet, N.},
   number = {18},
   year = {2022},
   doi = {10.1172/jci.insight.161783},
   url = {}
}
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Roveda, L., Bussolan, A., Braghin, F., Piga, D. (2022). Robot joint friction compensation learning enhanced by 6D virtual sensor. International Journal of Robust and Nonlinear Control 32(9), pp. 5741–5763.

Robot joint friction compensation learning enhanced by 6D virtual sensor

@ARTICLE{Roveda2022d,
   title = {Robot joint friction compensation learning enhanced by {6D} virtual sensor},
   journal = {International Journal of Robust and Nonlinear Control},
   volume = {32},
   author = {Roveda, L. and Bussolan, A. and Braghin, F. and Piga, D.},
   number = {9},
   pages = {5741--5763},
   year = {2022},
   doi = {10.1002/rnc.6108},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.6108}
}
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Roveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Shahid, A.A., Bucca, G., Piga, D. (2022). Robot end-effector mounted camera pose optimization in object detection-based tasks. Journal of Intelligent & Robotic Systems 104(1), 16.

Robot end-effector mounted camera pose optimization in object detection-based tasks

@ARTICLE{Roveda2022e,
   title = {Robot end-effector mounted camera pose optimization in object detection-based tasks},
   journal = {Journal of Intelligent & Robotic Systems},
   volume = {104},
   author = {Roveda, L. and Maroni, M. and Mazzuchelli, L. and Praolini, L. and Shahid, A.A. and Bucca, G. and Piga, D.},
   number = {1},
   pages = {16},
   year = {2022},
   doi = {10.1007/s10846-021-01558-0},
   url = {}
}
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Roveda, L., Pesenti, M., Rossi, M., Covarrubias, M., Galluzzi, C., Combi, S., Pedrocchi, A., Braghin, F., Gandolla, M. (2022). User-centered back-support exoskeleton: design and prototyping. Procedia CIRP 107, pp. 522–527.

User-centered back-support exoskeleton: design and prototyping

@ARTICLE{Roveda2022i,
   title = {User-centered back-support exoskeleton: design and prototyping},
   journal = {Procedia {CIRP}},
   volume = {107},
   author = {Roveda, L. and Pesenti, M. and Rossi, M. and Covarrubias, M. and Galluzzi, C. and Combi, S. and Pedrocchi, A. and Braghin, F. and Gandolla, M.},
   pages = {522--527},
   year = {2022},
   doi = {10.1016/j.procir.2022.05.019},
   url = {}
}
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Roveda, L., Testa, A., Shahid, A.A., Braghin, F., Piga, D. (2022). Q-learning-based model predictive variable impedance control for physical human-robot collaboration. Artificial Intelligence, 103771.

Q-learning-based model predictive variable impedance control for physical human-robot collaboration

@ARTICLE{Roveda2022a,
   title = {Q-learning-based model predictive variable impedance control for physical human-robot collaboration},
   journal = {Artificial Intelligence},
   author = {Roveda, L. and Testa, A. and Shahid, A.A. and Braghin, F. and Piga, D.},
   pages = {103771},
   year = {2022},
   doi = {10.1016/j.artint.2022.103771},
   url = {}
}
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Scutari, M. (2022). Comments on: Hybrid Semiparametric Bayesian Networks. TEST 31, pp. 328–330.

Comments on: Hybrid Semiparametric Bayesian Networks

@ARTICLE{scutari22d,
   title = {Comments on: {H}ybrid {S}emiparametric {B}ayesian {N}etworks},
   journal = {{TEST}},
   volume = {31},
   author = {Scutari, M.},
   pages = {328--330},
   year = {2022},
   doi = {10.1007/s11749-022-00818-x},
   url = {}
}
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Scutari, M., Marquis, C., Azzimonti, L. (2022). Using mixed-effects models to learn bayesian networks from related data sets. In Salmerón, A., Rumı́, R. (Eds), Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186, JMLR.org, pp. 73–84.

Using mixed-effects models to learn bayesian networks from related data sets

@INPROCEEDINGS{scutari2022a,
   title = {Using mixed-effects models to learn bayesian networks from related data sets},
   editor = {Salmer\'on, A. and Rumı́, R.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {186},
   booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models},
   author = {Scutari, M. and Marquis, C. and Azzimonti, L.},
   pages = {73--84},
   year = {2022},
   doi = {},
   url = {https://proceedings.mlr.press/v186/scutari22a.html}
}
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Scutari, M., Panero, F., Proissl, M. (2022). Achieving fairness with a simple ridge penalty. Statistics and Computing 32(5), 77.

Achieving fairness with a simple ridge penalty

@ARTICLE{scutari22a,
   title = {Achieving fairness with a simple ridge penalty},
   journal = {Statistics and Computing},
   volume = {32},
   author = {Scutari, M. and Panero, F. and Proissl, M.},
   number = {5},
   pages = {77},
   year = {2022},
   doi = {10.1007/s11222-022-10143-w},
   url = {}
}
Download
Shahid, A.A., Piga, D., Braghin, F., Roveda, L. (2022). Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning. Autonomous Robots 46(3), pp. 483–498.

Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning

@ARTICLE{Roveda2022b,
   title = {Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning},
   journal = {Autonomous Robots},
   volume = {46},
   author = {Shahid, A.A. and Piga, D. and Braghin, F. and Roveda, L.},
   number = {3},
   pages = {483--498},
   year = {2022},
   doi = {10.1007/s10514-022-10034-z},
   url = {}
}
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Slavakis, K., Shetty, G.N., Cannelli, L., Scutari, G., Nakarmi, U., Ying, L. (2022). Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-MRI case. IEEE Transactions on Computational Imaging 8, pp. 133–147.

Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-MRI case

@ARTICLE{cannelli2022a,
   title = {Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-{MRI} case},
   journal = {{IEEE} Transactions on Computational Imaging},
   volume = {8},
   author = {Slavakis, K. and Shetty, G.N. and Cannelli, L. and Scutari, G. and Nakarmi, U. and Ying, L.},
   pages = {133--147},
   year = {2022},
   doi = {10.1109/TCI.2022.3148062},
   url = {}
}
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Szehr, O., Zarouf, R. (2022). On the asymptotic behavior of Jacobi polynomials with first varying parameter. Journal of Approximation Theory (277), 105702.

On the asymptotic behavior of Jacobi polynomials with first varying parameter

@ARTICLE{szehr2022a,
   title = {On the asymptotic behavior of {J}acobi polynomials with first varying parameter},
   journal = {Journal of Approximation Theory},
   author = {Szehr, O. and Zarouf, R.},
   number = {277},
   pages = {105702},
   year = {2022},
   doi = {10.1016/j.jat.2022.105702},
   url = {}
}
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Zaffalon, M., Antonucci, A., Cabañas, R., Huber, D., Azzimonti, D. (2022). Bounding counterfactuals under selection bias. In Salmerón, A., Rumí, R. (Eds), Proceedings of PGM 2022, PMLR 186, JMLR.org, pp. 289–300.

Bounding counterfactuals under selection bias

@INPROCEEDINGS{zaffalon2022a,
   title = {Bounding counterfactuals under selection bias},
   editor = {Salmerón, A. and Rumí, R.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {186},
   booktitle = {Proceedings of {PGM} 2022},
   author = {Zaffalon, M. and Antonucci, A. and Cabañas, R. and Huber, D. and Azzimonti, D.},
   pages = {289--300},
   year = {2022},
   doi = {},
   url = {https://proceedings.mlr.press/v186/zaffalon22a.html}
}
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top

2021

Antonucci, A., Facchini, A., Mattei, L. (2021). Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption. In 4th Workshop on Tractable Probabilistic Modeling (TPM 2021 co-located with UAI 2021).

Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption

@INPROCEEDINGS{antonucci2021e,
   title = {Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption},
   booktitle = {4th Workshop on Tractable Probabilistic Modeling ({TPM} 2021 {c}o-{l}ocated {w}ith {UAI} 2021)},
   author = {Antonucci, A. and Facchini, A. and Mattei, L.},
   year = {2021},
   doi = {10.48550/arXiv.2107.12130},
   url = {}
}
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Antonucci, A., Mangili, F., Bonesana, C., Adorni, G. (2021). A new score for adaptive tests in Bayesian and credal networks. In Vejnarová, J., Wilson, N. (Eds), Symbolic and Quantitative Approaches to Reasoning With Uncertainty, Springer International Publishing, Cham, pp. 399–412.

A new score for adaptive tests in Bayesian and credal networks

@INPROCEEDINGS{antonucci2021c,
   title = {A new score for adaptive tests in {B}ayesian and credal networks},
   editor = {Vejnarov\'a, J. and Wilson, N.},
   publisher = {Springer International Publishing},
   address = {Cham},
   booktitle = {Symbolic and Quantitative Approaches to Reasoning With Uncertainty},
   author = {Antonucci, A. and Mangili, F. and Bonesana, C. and Adorni, G.},
   pages = {399--412},
   year = {2021},
   doi = {10.1007/978-3-030-86772-0_29},
   url = {}
}
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Azzimonti, D., Rottondi, C., Giusti, A., Tornatore, M., Bianco, A. (2021). Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]. IEEE/OSA Journal of Optical Communications and Networking 13(1), pp. A56–A66.

Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]

@ARTICLE{azzimontid2021,
   title = {Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [invited]},
   journal = {{IEEE/OSA} Journal of Optical Communications and Networking},
   volume = {13},
   author = {Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.},
   number = {1},
   pages = {A56--A66},
   year = {2021},
   doi = {10.1364/JOCN.401918},
   url = {}
}
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Bemporad, A., Piga, D. (2021). Global optimization based on active preference learning with radial basis functions. Machine Learning 110, pp. 417–448.

Global optimization based on active preference learning with radial basis functions

@ARTICLE{piga2021a,
   title = {Global optimization based on active preference learning with radial basis functions},
   journal = {Machine Learning},
   publisher = {Springer},
   volume = {110},
   author = {Bemporad, A. and Piga, D.},
   pages = {417--448},
   year = {2021},
   doi = {10.1007/s10994-020-05935-y},
   url = {}
}
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Benavoli, A., Azzimonti, D., Piga, D. (2021). Preferential bayesian optimisation with skew gaussian processes. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '21, Association for Computing Machinery, New York, NY, USA, pp. 1842–1850.

Preferential bayesian optimisation with skew gaussian processes

@INPROCEEDINGS{azzimontid2021b,
   title = {Preferential bayesian optimisation with skew gaussian processes},
   publisher = {Association for Computing Machinery},
   address = {New York, NY, USA},
   series = {GECCO '21},
   booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
   author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
   pages = {1842--1850},
   year = {2021},
   doi = {10.1145/3449726.3463128},
   url = {}
}
Download
Benavoli, A., Azzimonti, D., Piga, D. (2021). A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes. Machine Learning 110(11), pp. 3095–3133.

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes

@ARTICLE{azzimontid2021c,
   title = {A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes},
   journal = {Machine Learning},
   volume = {110},
   author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
   number = {11},
   pages = {3095--3133},
   year = {2021},
   doi = {10.1007/s10994-021-06039-x},
   url = {}
}
Download
Benavoli, A., Corani, G. (2021). State Space approximation of Gaussian Processes for time-series forecasting. In Advanced Analytics and Learning on Temporal Data, Springer International Publishing, pp. 21–35.

State Space approximation of Gaussian Processes for time-series forecasting

@INPROCEEDINGS{corani2021b,
   title = {State {S}pace approximation of {G}aussian {P}rocesses for time-series forecasting},
   journal = {Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th {ECML} {PKDD} Workshop, {AALTD} 2021},
   publisher = {Springer International Publishing},
   booktitle = {Advanced Analytics and Learning on Temporal Data},
   author = {Benavoli, A. and Corani, G.},
   pages = {21--35},
   year = {2021},
   doi = {10.1007/978-3-030-91445-5_2},
   url = {}
}
Download
Benavoli, A., Facchini, A., Zaffalon, M. (2021). The weirdness theorem and the origin of quantum paradoxes. Foundations of Physics 51(5), 95.

The weirdness theorem and the origin of quantum paradoxes

@ARTICLE{benavoli2021b,
   title = {The weirdness theorem and the origin of quantum paradoxes},
   journal = {Foundations of Physics},
   volume = {51},
   author = {Benavoli, A., Facchini, A., Zaffalon, M.},
   number = {5},
   pages = {95},
   year = {2021},
   doi = {10.1007/s10701-021-00499-w},
   url = {}
}
Download
Benavoli, A., Facchini, A., Zaffalon, M. (2021). Quantum indistinguishability through exchangeable desirable gambles. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, PMLR 147, JMLR.org, pp. 22–31.

Quantum indistinguishability through exchangeable desirable gambles

@INPROCEEDINGS{benavoli2021a,
   title = {Quantum indistinguishability through exchangeable desirable gambles},
   editor = {De Bock, J., Cano, A., Miranda, E., Moral, S.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {147},
   booktitle = {{ISIPTA} 2021},
   author = {Benavoli, A., Facchini, A., Zaffalon, M.},
   pages = {22--31},
   year = {2021},
   doi = {},
   url = {https://isipta21.sipta.org/papers.html}
}
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Berjano, P., Langella, F., Ventriglia, L., Compagnone, D., Barletta, P., Huber, D., Mangili, F., Licandro, G., Galbusera, F., Cina, A., Bassani, T., Lamartina, C., Scaramuzzo, L., Bassani, R., Brayda-Bruno, M., Villafañe, J.H., Monti, L., Azzimonti, L. (2021). The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach. Journal of Personalized Medicine 11(12), 1377.

The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach

@ARTICLE{azzimonti2021b,
   title = {The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach},
   journal = {Journal of Personalized Medicine},
   volume = {11},
   author = {Berjano, P. and Langella, F. and Ventriglia, L. and Compagnone, D. and Barletta, P. and Huber, D. and Mangili, F. and Licandro, G. and Galbusera, F. and Cina, A. and Bassani, T. and Lamartina, C. and Scaramuzzo, L. and Bassani, R. and Brayda-Bruno, M. and Villafa\~ne, J.H. and Monti, L. and Azzimonti, L.},
   number = {12},
   pages = {1377},
   year = {2021},
   doi = {10.3390/jpm11121377},
   url = {}
}
Download
Bianchi, F., Breschi, V., Piga, D., Piroddi, L. (2021). Model structure selection for switched narx system identification: a randomized approach. Automatica 125, 109415.

Model structure selection for switched narx system identification: a randomized approach

@ARTICLE{piga2021c,
   title = {Model structure selection for switched narx system identification: a randomized approach},
   journal = {Automatica},
   volume = {125},
   author = {Bianchi, F. and Breschi, V. and Piga, D. and Piroddi, L.},
   pages = {109415},
   year = {2021},
   doi = {10.1016/j.automatica.2020.109415},
   url = {}
}
Download
Bini, F., Pica, A., Azzimonti, L., Giusti, A., Ruinelli, L., Marinozzi, F., Trimboli, P. (2021). Artificial intelligence in thyroid field. A comprehensive review. Cancers 13(19), 4740.

Artificial intelligence in thyroid field. A comprehensive review

@ARTICLE{azzimonti2021a,
   title = {Artificial intelligence in thyroid field. A comprehensive review},
   journal = {Cancers},
   volume = {13},
   author = {Bini, F. and Pica, A. and Azzimonti, L. and Giusti, A. and Ruinelli, L. and Marinozzi, F. and Trimboli, P.},
   number = {19},
   pages = {4740},
   year = {2021},
   doi = {10.3390/cancers13194740},
   url = {https://www.mdpi.com/2072-6694/13/19/4740}
}
Download
Bodewes, T., Scutari, M. (2021). Learning bayesian networks from incomplete data with the node-averaged likelihood. International Journal of Approximate Reasoning 138, pp. 145–160.

Learning bayesian networks from incomplete data with the node-averaged likelihood

@ARTICLE{scutari21d,
   title = {Learning bayesian networks from incomplete data with the node-averaged likelihood},
   journal = {International Journal of Approximate Reasoning},
   volume = {138},
   author = {Bodewes, T. and Scutari, M.},
   pages = {145--160},
   year = {2021},
   doi = {10.1016/j.ijar.2021.07.015},
   url = {}
}
Download
Bonesana, C., Mangili, F., Antonucci, A. (2021). ADAPQUEST: a software for web-based adaptive questionnaires based on Bayesian networks. In AI4EDU: Artificial Intelligence for Education (@ Ijcai2021), Virtual Event.

ADAPQUEST: a software for web-based adaptive questionnaires based on Bayesian networks

@INPROCEEDINGS{bonesana2021a,
   title = {{ADAPQUEST}: a software for web-based adaptive questionnaires based on {B}ayesian networks},
   address = {Virtual Event},
   booktitle = {{AI4EDU}: Artificial Intelligence for Education (@ Ijcai2021)},
   author = {Bonesana, C. and Mangili, F. and Antonucci, A.},
   year = {2021},
   doi = {10.48550/arXiv.2112.14476},
   url = {}
}
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Bregoli, A., Scutari, M., Stella, F. (2021). A constraint-based algorithm for the structural learning of continuous-time bayesian networks. International Journal of Approximate Reasoning 138, pp. 105–122.

A constraint-based algorithm for the structural learning of continuous-time bayesian networks

@ARTICLE{scutari21e,
   title = {A constraint-based algorithm for the structural learning of continuous-time bayesian networks},
   journal = {International Journal of Approximate Reasoning},
   volume = {138},
   author = {Bregoli, A. and Scutari, M. and Stella, F.},
   pages = {105--122},
   year = {2021},
   doi = {10.1016/j.ijar.2021.08.005},
   url = {}
}
Download
Cabañas, R., Antonucci, A. (2021). CREPO: an open repository to benchmark credal network algorithms. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Eds), International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA-2021) 147, JMLR.org.

CREPO: an open repository to benchmark credal network algorithms

@INPROCEEDINGS{cabanas2021b,
   title = {{CREPO}: an open repository to benchmark credal network algorithms},
   editor = {De Bock, J. and Cano, A. and Miranda, E. and Moral, S.},
   publisher = {JMLR.org},
   volume = {147},
   booktitle = {International Symposium on Imprecise Probabilities: Theories and Applications ({ISIPTA}-2021)},
   author = {Caba\~nas, R. and Antonucci, A.},
   year = {2021},
   doi = {},
   url = {http://proceedings.mlr.press/v147/cabanas21a/cabanas21a.pdf}
}
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Casanova, A., Benavoli, A., Zaffalon, M. (2021). Nonlinear desirability as a linear classification problem. In Proceedings of Machine Learning Research 147, pp. 617–71.

Nonlinear desirability as a linear classification problem

@INPROCEEDINGS{casanova2021c,
   title = {Nonlinear desirability as a linear classification problem},
   volume = {147},
   booktitle = {Proceedings of Machine Learning Research},
   author = {Casanova, A. and Benavoli, A. and Zaffalon, M.},
   pages = {617--71},
   year = {2021},
   doi = {},
   url = {https://proceedings.mlr.press/v147/casanova21a.html}
}
Download
Casanova, A., Kohlas, J., Zaffalon, M. (2021). Algebras of sets and coherent sets of gambles. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Springer International Publishing, pp. 603–615.

Algebras of sets and coherent sets of gambles

@INPROCEEDINGS{casanova2021d,
   title = {Algebras of sets and coherent sets of gambles},
   publisher = {Springer International Publishing},
   booktitle = {Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty},
   author = {Casanova, A. and Kohlas, J. and Zaffalon, M.},
   pages = {603--615},
   year = {2021},
   doi = {10.1007/978-3-030-86772-0_43},
   url = {}
}
Download
Casanova, A., Miranda, E., Zaffalon, M. (2021). Joint desirability foundations of social choice and opinion pooling. Annals of Mathematics and Artificial Intelligence 89(10–11), pp. 965–1011.

Joint desirability foundations of social choice and opinion pooling

@ARTICLE{casanova2021a,
   title = {Joint desirability foundations of social choice and opinion pooling},
   journal = {Annals of Mathematics and Artificial Intelligence},
   volume = {89},
   author = {Casanova, A. and Miranda, E. and Zaffalon, M.},
   number = {10--11},
   pages = {965--1011},
   year = {2021},
   doi = {10.1007/s10472-021-09733-7},
   url = {}
}
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Corani, G., Benavoli, A., Zaffalon, M. (2021). Time series forecasting with Gaussian Processes needs priors. In European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 103–117.

Time series forecasting with Gaussian Processes needs priors

@INPROCEEDINGS{corani2021a,
   title = {Time series forecasting with {G}aussian {P}rocesses needs priors},
   booktitle = {European Conference on Machine Learning and Knowledge Discovery in Databases},
   author = {Corani, G. and Benavoli, A. and Zaffalon, M.},
   pages = {103--117},
   year = {2021},
   doi = {10.1007/978-3-030-86514-6_7},
   url = {}
}
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Forgione, M., Piga, D. (2021). dynoNet: a neural network architecture for learning dynamical systems. International Journal of Adaptive Control and Signal Processing 35, pp. 612–626.

dynoNet: a neural network architecture for learning dynamical systems

@ARTICLE{forgione2021a,
   title = {{dynoNet}: a neural network architecture for learning dynamical systems},
   journal = {International Journal of Adaptive Control and Signal Processing},
   volume = {35},
   author = {Forgione, M. and Piga, D.},
   pages = {612--626},
   year = {2021},
   doi = {10.1002/acs.3216},
   url = {}
}
Download
Forgione, M., Piga, D. (2021). Continuous-time system identification with neural networks: model structures and fitting criteria. European Journal of Control 59, pp. 69–81.

Continuous-time system identification with neural networks: model structures and fitting criteria

@ARTICLE{forgione2021b,
   title = {Continuous-time system identification with neural networks: model structures and fitting criteria},
   journal = {European Journal of Control},
   volume = {59},
   author = {Forgione, M. and Piga, D.},
   pages = {69--81},
   year = {2021},
   doi = {10.1016/j.ejcon.2021.01.008},
   url = {}
}
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Dalla Gasperina, S., Roveda, L., Pedrocchi, A., Braghin, F., Gandolla, M. (2021). Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons. Frontiers in Robotics and AI 8.

Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons

@ARTICLE{Roveda2021e,
   title = {Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons},
   journal = {Frontiers in Robotics and {AI}},
   volume = {8},
   author = {Dalla Gasperina, S. and Roveda, L. and Pedrocchi, A. and Braghin, F. and Gandolla, M.},
   year = {2021},
   doi = {10.3389/frobt.2021.745018},
   url = {}
}
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Geluykens, J., Mitrovic, S., Vázquez, C.O., Laino, T., Vaucher, A.C., Weerdt, J.D. (2021). Neural machine translation for conditional generation of novel procedures. In 54th Hawaii International Conference on System Sciences, HICSS 2021, Kauai, Hawaii, Usa, January 5, 2021, ScholarSpace, pp. 1–10.

Neural machine translation for conditional generation of novel procedures

@INPROCEEDINGS{mitrovic2021b,
   title = {Neural machine translation for conditional generation of novel procedures},
   publisher = {ScholarSpace},
   booktitle = {54th Hawaii International Conference on System Sciences, {HICSS} 2021, Kauai, Hawaii, Usa, January 5, 2021},
   author = {Geluykens, J. and Mitrovic, S. and V\'azquez, C.O. and Laino, T. and Vaucher, A.C. and Weerdt, J.D.},
   pages = {1--10},
   year = {2021},
   doi = {},
   url = {http://hdl.handle.net/10125/70744}
}
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Gómez-Olmedo, M., Cabañas, R., Cano, A., Moral, S., Retamero, O.P. (2021). Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models. International Journal of Intelligent Systems 36(11), pp. 6913–6943.

Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models

@ARTICLE{cabanas2021c,
   title = {Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models},
   journal = {International Journal of Intelligent Systems},
   volume = {36},
   author = {G\'omez-Olmedo, M. and Caba\~nas, R. and Cano, A. and Moral, S. and Retamero, O.P.},
   number = {11},
   pages = {6913--6943},
   year = {2021},
   doi = {10.1002/int.22573},
   url = {}
}
Download
Grasso, G., Gregorio, A.D., Mavkov, B., Piga, D., Labate, G.F.D., Danani, A., Deriu, M.A. (2021). Fragmented blind docking: a novel protein–ligand binding prediction protocol. Journal of Biomolecular Structure and Dynamics 40(24), pp. 13472–13481.

Fragmented blind docking: a novel protein–ligand binding prediction protocol

@ARTICLE{piga2021f,
   title = {Fragmented blind docking: a novel protein--ligand binding prediction protocol},
   journal = {Journal of Biomolecular Structure and Dynamics},
   publisher = {Taylor & Francis},
   volume = {40},
   author = {Grasso, G. and Gregorio, A.D. and Mavkov, B. and Piga, D. and Labate, G.F.D. and Danani, A. and Deriu, M.A.},
   number = {24},
   pages = {13472--13481},
   year = {2021},
   doi = {10.1080/07391102.2021.1988709},
   url = {}
}
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Halasz, G., Sperti, M., Villani, M., Michelucci, U., Agostoni, P., Biagi, A., Rossi, L., Botti, A., Mari, C., Maccarini, M., Pura, F., Roveda, L., Nardecchia, A., Mottola, E., Nolli, M., Salvioni, E., Mapelli, M., Deriu, M.A., Piga, D., Piepoli, M. (2021). A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score. Journal of Medical Internet Research 23(5), e29058.

A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score

@ARTICLE{piga2021g,
   title = {A machine learning approach for mortality prediction in covid-19 pneumonia: development and evaluation of the piacenza score},
   journal = {Journal of Medical Internet Research},
   volume = {23},
   author = {Halasz, G. and Sperti, M. and Villani, M. and Michelucci, U. and Agostoni, P. and Biagi, A. and Rossi, L. and Botti, A. and Mari, C. and Maccarini, M. and Pura, F. and Roveda, L. and Nardecchia, A. and Mottola, E. and Nolli, M. and Salvioni, E. and Mapelli, M. and Deriu, M.A. and Piga, D. and Piepoli, M.},
   number = {5},
   pages = {e29058},
   year = {2021},
   doi = {10.2196/29058},
   url = {}
}
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Kania, L., Schürch, M., Azzimonti, D., Benavoli, A. (2021). Sparse information filter for fast gaussian process regression. In Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (Eds), Machine Learning and Knowledge Discovery in Databases. Research Track, Springer International Publishing, Cham, pp. 527–542.

Sparse information filter for fast gaussian process regression

@INPROCEEDINGS{schurch2021a,
   title = {Sparse information filter for fast gaussian process regression},
   editor = {Oliver, N. and P\'erez-Cruz, F. and Kramer, S. and Read, J. and Lozano, J.A.},
   publisher = {Springer International Publishing},
   address = {Cham},
   booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track},
   author = {Kania, L. and Sch\"urch, M. and Azzimonti, D. and Benavoli, A.},
   pages = {527--542},
   year = {2021},
   doi = {10.1007/978-3-030-86523-8_32},
   url = {}
}
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Kanjirangat, V., Rinaldi, F. (2021). Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information. Journal of Biomedical Informatics, 103893.

Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information

@ARTICLE{vani2021b,
   title = {Enhancing {B}iomedical {R}elation {E}xtraction with {T}ransformer {M}odels using {S}hortest {D}ependency {P}ath {F}eatures and {T}riplet {I}nformation},
   journal = {Journal of Biomedical Informatics},
   publisher = {Elsevier},
   author = {Kanjirangat, V. and Rinaldi, F.},
   pages = {103893},
   year = {2021},
   doi = {10.1016/j.jbi.2021.103893},
   url = {}
}
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Kohlas, J., Casanova, A., Zaffalon, M. (2021). Information algebras of coherent sets of gambles in general possibility spaces. In Proceedings of Machine Learning Research 147, pp. 191–200.

Information algebras of coherent sets of gambles in general possibility spaces

@INPROCEEDINGS{casanova2021b,
   title = {Information algebras of coherent sets of gambles in general possibility spaces},
   volume = {147},
   booktitle = {Proceedings of Machine Learning Research},
   author = {Kohlas, J. and Casanova, A. and Zaffalon, M.},
   pages = {191--200},
   year = {2021},
   doi = {},
   url = {http://proceedings.mlr.press/v147/kohlas21a/kohlas21a.pdf}
}
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Liew, B.X.W., Ford, J.J., Scutari, M., Hahne, A.J. (2021). How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data. PLoS ONE 16, pp. 1–16.

How does individualised physiotherapy work for people with low back pain? A Bayesian Network analysis using randomised controlled trial data

@ARTICLE{scutari21c,
   title = {How does individualised physiotherapy work for people with low back pain? A {B}ayesian {N}etwork analysis using randomised controlled trial data},
   journal = {{PLoS} {ONE}},
   volume = {16},
   author = {Liew, B.X.W. and Ford, J.J. and Scutari, M. and Hahne, A.J.},
   pages = {1--16},
   year = {2021},
   doi = {10.1371/journal.pone.0258515},
   url = {}
}
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Liew, B.X.W., Peolsson, A., Falla, D., Cleland, J.A., Scutari, M., Kierkegaard, M., r A Dedering, (2021). Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy. European Journal of Pain 25(5), pp. 1162–1172.

Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy

@ARTICLE{scutari21b,
   title = {Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy},
   journal = {European Journal of Pain},
   volume = {25},
   author = {Liew, B.X.W. and Peolsson, A. and Falla, D. and Cleland, J.A. and Scutari, M. and Kierkegaard, M. and r A Dedering, },
   number = {5},
   pages = {1162--1172},
   year = {2021},
   doi = {10.1002/ejp.1741},
   url = {}
}
Download
Llerena, J.V., Mauá, D.D., Antonucci, A. (2021). Cautious classification with data missing not at random using generative random forests. In Vejnarová, J., Wilson, N. (Eds), Symbolic and Quantitative Approaches to Reasoning With Uncertainty, Springer International Publishing, Cham, pp. 284–298.

Cautious classification with data missing not at random using generative random forests

@INPROCEEDINGS{antonucci2021b,
   title = {Cautious classification with data missing not at random using generative random forests},
   editor = {Vejnarov\'a, J. and Wilson, N.},
   publisher = {Springer International Publishing},
   address = {Cham},
   booktitle = {Symbolic and Quantitative Approaches to Reasoning With Uncertainty},
   author = {Llerena, J.V. and Mau\'a, D.D. and Antonucci, A.},
   pages = {284--298},
   year = {2021},
   doi = {10.1007/978-3-030-86772-0_21},
   url = {}
}
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Maggioni, B., Marescotti, E., Zanchettin, M., Piga, D., Roveda, L. (2021). Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives. In .

Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives

@INPROCEEDINGS{Roveda2021g,
   title = {Velocity planning of a robotic task enhanced by fuzzy logic and dynamic movement primitives},
   journal = {{ARCI} 2021},
   author = {Maggioni, B. and Marescotti, E. and Zanchettin, M. and Piga, D. and Roveda, L.},
   year = {2021},
   doi = {},
   url = {}
}
Download
Pedrero-Martin, Y., Falla, D., Martinez-Calderon, J., Liew, B.X.W., Scutari, M., Luque-Suarez, A. (2021). Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders. European Spine Journal 20(6), pp. 1689–1698.

Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders

@ARTICLE{scutari21a,
   title = {Self-efficacy beliefs mediate the association between pain intensity and pain interference in acute/subacute whiplash-associated disorders},
   journal = {European Spine Journal},
   volume = {20},
   author = {Pedrero-Martin, Y. and Falla, D. and Martinez-Calderon, J. and Liew, B.X.W. and Scutari, M. and Luque-Suarez, A.},
   number = {6},
   pages = {1689--1698},
   year = {2021},
   doi = {10.1007/s00586-021-06731-5},
   url = {}
}
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Masegosa, A.R., Cabañas, R., Langseth, H., Nielsen, T.D., Salmerón, A. (2021). Probabilistic models with deep neural networks. Entropy 23(1), 117.

Probabilistic models with deep neural networks

@ARTICLE{cabanas2021a,
   title = {Probabilistic models with deep neural networks},
   journal = {Entropy},
   volume = {23},
   author = {Masegosa, A.R. and Caba\~nas, R. and Langseth, H. and Nielsen, T.D. and Salmer\'on, A.},
   number = {1},
   pages = {117},
   year = {2021},
   doi = {10.3390/e23010117},
   url = {}
}
Download
Mejari, M., Mavkov, B., Forgione, M., Piga, D. (2021). An integral architecture for identification of continuous-time state-space lpv models. In 4th IFAC Workshop on Linear Parameter-Varying Systems LPVS 2021 54(8), Milan, Italy, pp. 7–12.

An integral architecture for identification of continuous-time state-space lpv models

@INPROCEEDINGS{mejari2021a,
   title = {An integral architecture for identification of continuous-time state-space lpv models},
   journal = {{IFAC}-{PapersOnLine}},
   address = {Milan, Italy},
   volume = {54},
   booktitle = {4th {IFAC} Workshop on Linear Parameter-Varying Systems {LPVS} 2021},
   author = {Mejari, M. and Mavkov, B. and Forgione, M. and Piga, D.},
   number = {8},
   pages = {7--12},
   year = {2021},
   doi = {10.1016/j.ifacol.2021.08.573},
   url = {}
}
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Mellace, S., Kanjirangat, V., Antonucci, A. (2021). Relation clustering in narrative knowledge graphs. In AI4Narratives Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 20).

Relation clustering in narrative knowledge graphs

@INPROCEEDINGS{vani2021a,
   title = {Relation clustering in narrative knowledge graphs},
   booktitle = {{AI4Narratives} Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence ({IJCAI}-{PRICAI} 20)},
   author = {Mellace, S. and Kanjirangat, V. and Antonucci, A.},
   year = {2021},
   doi = {},
   url = {http://ceur-ws.org/Vol-2794/paper5.pdf}
}
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Michelucci, U., Sperti, M., Piga, D., Venturini, F., Deriu, M.A. (2021). A model-agnostic algorithm for bayes error determination in binary classification. Algorithms 14(11), 301.

A model-agnostic algorithm for bayes error determination in binary classification

@ARTICLE{piga2021d,
   title = {A model-agnostic algorithm for bayes error determination in binary classification},
   journal = {Algorithms},
   volume = {14},
   author = {Michelucci, U. and Sperti, M. and Piga, D. and Venturini, F. and Deriu, M.A.},
   number = {11},
   pages = {301},
   year = {2021},
   doi = {10.3390/a14110301},
   url = {}
}
Download
Awal, M.R., Cao, R., Lee, R.K., Mitrovic, S. (2021). Angrybert: joint learning target and emotion for hate speech detection. In Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R.K., Reddy, P.K., Srivastava, J., Chakraborty, T. (Eds), Advances in Knowledge Discovery and Data Mining - 25th Pacific-asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I, Lecture Notes in Computer Science 12712, Springer, pp. 701–713.

Angrybert: joint learning target and emotion for hate speech detection

@INPROCEEDINGS{mitrovic2021a,
   title = {Angrybert: joint learning target and emotion for hate speech detection},
   editor = {Karlapalem, K. and Cheng, H. and Ramakrishnan, N. and Agrawal, R.K. and Reddy, P.K. and Srivastava, J. and Chakraborty, T.},
   publisher = {Springer},
   series = {Lecture Notes in Computer Science},
   volume = {12712},
   booktitle = {Advances in Knowledge Discovery and Data Mining - 25th Pacific-{a}sia Conference, {PAKDD} 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I},
   author = {Awal, M.R. and Cao, R. and Lee, R.K. and Mitrovic, S.},
   pages = {701--713},
   year = {2021},
   doi = {10.1007/978-3-030-75762-5_55},
   url = {}
}
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Piga, D., Forgione, M., Mejari, M. (2021). Deep learning with transfer functions: New applications in system identification. In Proceedings of the 19th IFAC Symposium System Identification: learning models for decision and control 54(7), pp. 415–420.

Deep learning with transfer functions: New applications in system identification

@INPROCEEDINGS{forgione2021c,
   title = {Deep learning with transfer functions: {N}ew applications in system identification},
   journal = {{IFAC}-{PapersOnLine}},
   volume = {54},
   booktitle = {Proceedings of the 19th {IFAC} Symposium System Identification: {l}earning {m}odels for {d}ecision and {c}ontrol},
   author = {Piga, D. and Forgione, M. and Mejari, M.},
   number = {7},
   pages = {415--420},
   year = {2021},
   doi = {10.1016/j.ifacol.2021.08.395},
   url = {}
}
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Ropero, R.F., Flores, M.J., Cabañas, R., Rumí, R. (2021). A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management. In Proceedings of the 15th Uai Conference on Bayesian Modeling Applications Workshop.

A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management

@INPROCEEDINGS{cabanas2021d,
   title = {A comparison bewteen elvira software and amidst toolbox in environmental data: a case study of flooding risk management},
   booktitle = {Proceedings of the 15th Uai Conference on Bayesian Modeling Applications Workshop},
   author = {Ropero, R.F. and Flores, M.J. and Caba\~nas, R. and Rum\'i, R.},
   year = {2021},
   doi = {},
   url = {}
}
Download
Roveda, L., Maggioni, B., Marescotti, E., Shahid, A., Zanchettin, M., Bemporad, A., Piga, D. (2021). Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks. IEEE Robotics and Automation Letters 6(4), pp. 6632–6639.

Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks

@ARTICLE{Roveda2021c,
   title = {Pairwise preferences-based optimization of a path-based velocity planner in robotic sealing tasks},
   journal = {{IEEE} Robotics and Automation Letters},
   volume = {6},
   author = {Roveda, L. and Maggioni, B. and Marescotti, E. and Shahid, A. and Zanchettin, M. and Bemporad, A. and Piga, D.},
   number = {4},
   pages = {6632--6639},
   year = {2021},
   doi = {10.1109/LRA.2021.3094479},
   url = {}
}
Download
Roveda, L., Maroni, M., Mazzuchelli, L., Praolini, L., Bucca, G., Piga, D. (2021). Enhancing object detection performance through sensor pose Definition with bayesian optimization. In , pp. 699–703.

Enhancing object detection performance through sensor pose Definition with bayesian optimization

@INPROCEEDINGS{Roveda2021h,
   title = {Enhancing object detection performance through sensor pose {D}efinition with bayesian optimization},
   journal = {Metrology2021},
   author = {Roveda, L. and Maroni, M. and Mazzuchelli, L. and Praolini, L. and Bucca, G. and Piga, D.},
   pages = {699--703},
   year = {2021},
   doi = {10.1109/METROIND4.0IOT51437.2021.9488517},
   url = {}
}
Download
Roveda, L., Piga, D. (2021). Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks. Autonomous Robots 45(3), pp. 371–388.

Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

@ARTICLE{Roveda2021a,
   title = {Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks},
   journal = {Autonomous Robots},
   volume = {45},
   author = {Roveda, L. and Piga, D.},
   number = {3},
   pages = {371--388},
   year = {2021},
   doi = {10.1007/s10514-021-09970-z},
   url = {}
}
Download
Roveda, L., Riva, D., Bucca, G., Piga, D. (2021). External joint torques estimation for a position-controlled manipulator employing an extended kalman filter. In , pp. 101–107.

External joint torques estimation for a position-controlled manipulator employing an extended kalman filter

@INPROCEEDINGS{Roveda2021i,
   title = {External joint torques estimation for a position-controlled manipulator employing an extended kalman filter},
   journal = {{UbiquitousRobots2021}},
   author = {Roveda, L. and Riva, D. and Bucca, G. and Piga, D.},
   pages = {101--107},
   year = {2021},
   doi = {10.1109/UR52253.2021.9494674},
   url = {}
}
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Roveda, L., Riva, D., Bucca, G., Piga, D. (2021). Sensorless optimal switching Impact/Force controller. IEEE Access 9, pp. 158167–158184.

Sensorless optimal switching Impact/Force controller

@ARTICLE{Roveda2021f,
   title = {Sensorless optimal switching {Impact/Force} controller},
   journal = {{IEEE} Access},
   volume = {9},
   author = {Roveda, L. and Riva, D. and Bucca, G. and Piga, D.},
   pages = {158167--158184},
   year = {2021},
   doi = {10.1109/ACCESS.2021.3131390},
   url = {}
}
Download
Roveda, L., Shahid, A., Iannacci, N., Piga, D. (2021). Sensorless optimal interaction control exploiting environment stiffness estimation. IEEE Transactions on Control System Technology 30(1), pp. 218–233.

Sensorless optimal interaction control exploiting environment stiffness estimation

@ARTICLE{Roveda2021b,
   title = {Sensorless optimal interaction control exploiting environment stiffness estimation},
   journal = {{IEEE} Transactions on Control System Technology},
   volume = {30},
   author = {Roveda, L. and Shahid, A. and Iannacci, N. and Piga, D.},
   number = {1},
   pages = {218--233},
   year = {2021},
   doi = {10.1109/TCST.2021.3061091},
   url = {}
}
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Selvi, D., Piga, D., Battistelli, G., Bemporad, A. (2021). Optimal direct data-driven control with stability guarantees. European Journal of Control 59, pp. 175–187.

Optimal direct data-driven control with stability guarantees

@ARTICLE{piga2021b,
   title = {Optimal direct data-driven control with stability guarantees},
   journal = {European Journal of Control},
   volume = {59},
   author = {Selvi, D. and Piga, D. and Battistelli, G. and Bemporad, A.},
   pages = {175--187},
   year = {2021},
   doi = {10.1016/j.ejcon.2020.09.005},
   url = {}
}
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Shahid, A., Sesin, J., Pecioski, D., Braghin, F., Piga, D., Roveda, L. (2021). Decentralized multi-agent control of a manipulator in continuous task learning. MDPI Applied Science 11(21), 10227.

Decentralized multi-agent control of a manipulator in continuous task learning

@ARTICLE{Roveda2021d,
   title = {Decentralized multi-agent control of a manipulator in continuous task learning},
   journal = {{MDPI} Applied Science},
   volume = {11},
   author = {Shahid, A. and Sesin, J. and Pecioski, D. and Braghin, F. and Piga, D. and Roveda, L.},
   number = {21},
   pages = {10227},
   year = {2021},
   doi = {10.3390/app112110227},
   url = {}
}
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Sommer, M., Rüegsegger, M., Szehr, O., Del Rio, G. (2021). Deep self-optimizing artificial intelligence for tactical analysis, training and optimization. In , NATO.

Deep self-optimizing artificial intelligence for tactical analysis, training and optimization

@INPROCEEDINGS{szehr2021b,
   title = {Deep self-optimizing artificial intelligence for tactical analysis, training and optimization},
   journal = {{MSG}-Symposium},
   publisher = {NATO},
   author = {Sommer, M. and R\"uegsegger, M. and Szehr, O. and Del Rio, G.},
   year = {2021},
   doi = {},
   url = {}
}
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Szehr, O., Zarouf, R. (2021). Explicit counterexamples to Schäffer's conjecture. Journal de Mathématiques Pures et Appliquées 146, pp. 1–30.

Explicit counterexamples to Schäffer's conjecture

@ARTICLE{szehr2021a,
   title = {Explicit counterexamples to {S}ch\"affer's conjecture},
   journal = {Journal {d}e Math\'ematiques Pures {e}t Appliqu\'ees},
   volume = {146},
   author = {Szehr, O. and Zarouf, R.},
   pages = {1--30},
   year = {2021},
   doi = {10.1016/j.matpur.2020.10.006},
   url = {}
}
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Termine, A., Antonucci, A., Primiero, G., Facchini, A. (2021). Logic and model checking by imprecise probabilistic interpreted systems. In Rosenfeld, A., Talmon, N. (Eds), Multi-Agent Systems. EUMAS 2021. Lecture Notes in Computer Science, Springer International Publishing, Cham, pp. 211–227.

Logic and model checking by imprecise probabilistic interpreted systems

@INPROCEEDINGS{antonucci2021d,
   title = {Logic and model checking by imprecise probabilistic interpreted systems},
   editor = {Rosenfeld, A. and Talmon, N.},
   publisher = {Springer International Publishing},
   address = {Cham},
   booktitle = {Multi-Agent Systems. {EUMAS} 2021. Lecture Notes in Computer Science},
   author = {Termine, A. and Antonucci, A. and Primiero, G. and Facchini, A.},
   pages = {211--227},
   year = {2021},
   doi = {10.1007/978-3-030-82254-5_13},
   url = {}
}
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Termine, A., Antonucci, A., Facchini, A., Primiero, G. (2021). Robust model checking with imprecise markov reward models. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, Proceedings of Machine Learning Research 147, JMLR.org, pp. 299–309.

Robust model checking with imprecise markov reward models

@INPROCEEDINGS{antonucci2021a,
   title = {Robust model checking with imprecise markov reward models},
   editor = {De Bock, J., Cano, A., Miranda, E., Moral, S.},
   publisher = {JMLR.org},
   series = {Proceedings of Machine Learning Research},
   volume = {147},
   booktitle = {{ISIPTA} 2021},
   author = {Termine, A. and Antonucci, A. and Facchini, A. and Primiero, G.},
   pages = {299--309},
   year = {2021},
   doi = {},
   url = {https://proceedings.mlr.press/v147/termine21a.html}
}
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Zaffalon, M., Antonucci, A., Cabañas, R. (2021). Causal expectation-maximisation. In WHY-21 @ NeurIPS 2021.

Causal expectation-maximisation

@INPROCEEDINGS{zaffalon2021c,
   title = {Causal expectation-maximisation},
   booktitle = {{WHY}-21 @ {NeurIPS} 2021},
   author = {Zaffalon, M. and Antonucci, A. and Cabañas, R.},
   year = {2021},
   doi = {},
   url = {https://why21.causalai.net/papers/WHY21_52.pdf}
}
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Zaffalon, M., Miranda, E. (2021). Desirability foundations of robust rational decision making. Synthese 198(27), pp. S6529–S6570.

Desirability foundations of robust rational decision making

@ARTICLE{zaffalon2019a,
   title = {Desirability foundations of robust rational decision making},
   journal = {Synthese},
   publisher = {Springer},
   volume = {198},
   author = {Zaffalon, M. and Miranda, E.},
   number = {27},
   pages = {S6529--S6570},
   year = {2021},
   doi = {10.1007/s11229-018-02010-x},
   url = {}
}
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Zaffalon, M., Miranda, E. (2021). The sure thing. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), ISIPTA 2021, PMLR 147, JMLR.org, pp. 342–351.

The sure thing

@INPROCEEDINGS{zaffalon2021a,
   title = {The sure thing},
   editor = {De Bock, J., Cano, A., Miranda, E., Moral, S.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {147},
   booktitle = {{ISIPTA} 2021},
   author = {Zaffalon, M., Miranda, E.},
   pages = {342--351},
   year = {2021},
   doi = {},
   url = {https://www.sipta.org/isipta21/pmlr/zaffalon21.pdf}
}
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Zhu, M., Bemporad, A., Piga, D. (2021). Preference-based MPC calibration. In 2021 European Control Conference (ECC), Napoli, Italy, pp. 638–645.

Preference-based MPC calibration

@INPROCEEDINGS{piga2021e,
   title = {Preference-based {MPC} calibration},
   address = {Napoli, Italy},
   booktitle = {2021 European Control Conference ({ECC})},
   author = {Zhu, M. and Bemporad, A. and Piga, D.},
   pages = {638--645},
   year = {2021},
   doi = {10.23919/ECC54610.2021.9654900},
   url = {}
}
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top

2020

Akata, Z., Ballier, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L.C., van Harmelen, F., van Hoof, H., van Riemsdijk, B., van Wynsberghe, A., Verbrugge, R., Berheij, B., Vossen, P., Welling, M. (2020). A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence. IEEE Computer 53, pp. 18–28.

A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence

@ARTICLE{Linda2020b,
   title = {A research agenda for hybrid intelligence: {A}ugmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence},
   journal = {{IEEE} Computer},
   volume = {53},
   author = {Akata, Z. and Ballier, D. and de Rijke, M. and Dignum, F. and Dignum, V. and Eiben, G. and Fokkens, A. and Grossi, D. and Hindriks, K. and Hoos, H. and Hung, H. and Jonker, C. and Monz, C. and Neerincx, M. and Oliehoek, F. and Prakken, H. and Schlobach, S. and van der Gaag, L.C. and van Harmelen, F. and van Hoof, H. and van Riemsdijk, B. and van Wynsberghe, A. and Verbrugge, R. and Berheij, B. and Vossen, P. and Welling, M.},
   pages = {18--28},
   year = {2020},
   doi = {10.1109/MC.2020.2996587},
   url = {}
}
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Antonucci, A., Tiotto, T. (2020). Approximate MMAP by marginal search. In Brawner, K.W., Barták, R., Bell, E. (Eds), Proceedings of the Thirty-third International Florida Artificial Intelligence Research Society Conference (FLAIRS-33), AAAI Press, North Miami Beach, Florida, USA, pp. 181–184.

Approximate MMAP by marginal search

@INPROCEEDINGS{antonucci2020a,
   title = {Approximate {MMAP} by marginal search},
   editor = {Brawner, K.W. and Bart\'ak, R. and Bell, E.},
   publisher = {AAAI Press},
   address = {North Miami Beach, Florida, USA},
   booktitle = {Proceedings of the Thirty-{t}hird International Florida Artificial Intelligence Research Society Conference ({FLAIRS}-33)},
   author = {Antonucci, A. and Tiotto, T.},
   pages = {181--184},
   year = {2020},
   doi = {},
   url = {}
}
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Azzimonti, L., Corani, G., Scutari, M. (2020). Structure learning from related data sets with a hierarchical Bayesian score. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, PMLR, pp. 5–16.

Structure learning from related data sets with a hierarchical Bayesian score

@INPROCEEDINGS{azzimonti2020a,
   title = {Structure learning from related data sets with a hierarchical {B}ayesian score},
   publisher = {PMLR},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
   author = {Azzimonti, L. and Corani, G. and Scutari, M.},
   pages = {5--16},
   year = {2020},
   doi = {},
   url = {http://proceedings.mlr.press/v138/azzimonti20a.html}
}
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Azzimonti, D., Rottondi, C., Giusti, A., Tornatore, M., Bianco, A. (2020). Active vs transfer learning approaches for qot estimation with small training datasets. In Optical Fiber Communication Conference (OFC 2020), Optical Society of America, M4E.1.

Active vs transfer learning approaches for qot estimation with small training datasets

@INPROCEEDINGS{azzimontid2020b,
   title = {Active vs transfer learning approaches for qot estimation with small training datasets},
   publisher = {Optical Society of America},
   booktitle = {Optical Fiber Communication Conference ({OFC} 2020)},
   author = {Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.},
   pages = {M4E.1},
   year = {2020},
   doi = {10.1364/OFC.2020.M4E.1},
   url = {}
}
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Azzimonti, D., Rottondi, C., Tornatore, M. (2020). Reducing probes for quality of transmission estimation in optical networks with active learning. J. Opt. Commun. Netw. 12(1), pp. A38–A48.

Reducing probes for quality of transmission estimation in optical networks with active learning

@ARTICLE{azzimontid2020a,
   title = {Reducing probes for quality of transmission estimation in optical networks with active learning},
   journal = {J. Opt. Commun. Netw.},
   publisher = {OSA},
   volume = {12},
   author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},
   number = {1},
   pages = {A38--A48},
   year = {2020},
   doi = {10.1364/JOCN.12.000A38},
   url = {}
}
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Benavoli, A., Azzimonti, D., Piga, D. (2020). Skew gaussian processes for classification. Machine Learning 109(9), pp. 1877–1902.

Skew gaussian processes for classification

@ARTICLE{azzimontid2020c,
   title = {Skew gaussian processes for classification},
   journal = {Machine Learning},
   volume = {109},
   author = {Benavoli, A. and Azzimonti, D. and Piga, D.},
   number = {9},
   pages = {1877--1902},
   year = {2020},
   doi = {10.1007/s10994-020-05906-3},
   url = {}
}
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Bodewes, T., Scutari, M. (2020). Identifiability and consistency of bayesian network structure learning from incomplete data. Proceedings of Machine Learning Research (PGM 2020) 138, pp. 29–40.

Identifiability and consistency of bayesian network structure learning from incomplete data

@ARTICLE{scutari20c,
   title = {Identifiability and consistency of bayesian network structure learning from incomplete data},
   journal = {Proceedings of Machine Learning Research ({PGM} 2020)},
   volume = {138},
   author = {Bodewes, T. and Scutari, M.},
   pages = {29--40},
   year = {2020},
   doi = {},
   url = {https://proceedings.mlr.press/v138/bodewes20a.html}
}
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Bregoli, A., Scutari, M., Stella, F. (2020). Constraint-based learning for continuous-time bayesian networks. Proceedings of Machine Learning Research (PGM 2020) 138, pp. 41–52.

Constraint-based learning for continuous-time bayesian networks

@ARTICLE{scutari20g,
   title = {Constraint-based learning for continuous-time bayesian networks},
   journal = {Proceedings of Machine Learning Research ({PGM} 2020)},
   volume = {138},
   author = {Bregoli, A. and Scutari, M. and Stella, F.},
   pages = {41--52},
   year = {2020},
   doi = {},
   url = {}
}
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Breschi, V., Mejari, M. (2020). Shrinkage strategies for structure selection and identification of piecewise affine models. In 2020 59th Ieee Conference on Decision and Control (cdc), pp. 1626–1631.

Shrinkage strategies for structure selection and identification of piecewise affine models

@INPROCEEDINGS{mejari2020d,
   title = {Shrinkage strategies for structure selection and identification of piecewise affine models},
   booktitle = {2020 59th Ieee Conference on Decision and Control ({c}dc)},
   author = {Breschi, V. and Mejari, M.},
   pages = {1626--1631},
   year = {2020},
   doi = {10.1109/CDC42340.2020.9303927},
   url = {}
}
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Briganti, G., Scutari, M., Linkowski, P. (2020). A machine learning approach to relationships among alexithymia components. Psychiatria Danubina 32(Suppl. 1), pp. 180–187.

A machine learning approach to relationships among alexithymia components

@ARTICLE{scutari20e,
   title = {A machine learning approach to relationships among alexithymia components},
   journal = {Psychiatria Danubina},
   volume = {32},
   author = {Briganti, G. and Scutari, M. and Linkowski, P.},
   number = {Suppl. 1},
   pages = {180--187},
   year = {2020},
   doi = {},
   url = {}
}
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Cabañas, R., Antonucci, A., Huber, D., Zaffalon, M. (2020). Credici: a java library for causal inference by credal networks. In Proceedings of the 10th International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.

Credici: a java library for causal inference by credal networks

@INPROCEEDINGS{cabanas2020a,
   title = {Credici: a java library for causal inference by credal networks},
   publisher = {PMLR},
   address = {Aalborg, Denmark},
   series = {Proceedings of Machine Learning Research},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
   author = {Caba\~nas, R. and Antonucci, A. and Huber, D. and Zaffalon, M.},
   year = {2020},
   doi = {},
   url = {}
}
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Cabañas, R., Cózar, J., Salmerón, A., Masegosa, A.R. (2020). Probabilistic graphical models with neural networks in inferpy. In Proceedings of the 10th International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 601–604.

Probabilistic graphical models with neural networks in inferpy

@INPROCEEDINGS{cabanas2020b,
   title = {Probabilistic graphical models with neural networks in inferpy},
   publisher = {PMLR},
   address = {Aalborg, Denmark},
   series = {Proceedings of Machine Learning Research},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
   author = {Caba\~nas, R. and C\'ozar, J. and Salmer\'on, A. and Masegosa, A.R.},
   pages = {601--604},
   year = {2020},
   doi = {},
   url = {https://proceedings.mlr.press/v138/cabanas20b}
}
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Cannelli, L., Facchinei, F., Scutari, G., Kungurtsev, V. (2020). Asynchronous optimization over graphs: linear convergence under error bound conditions. IEEE Transactions on Automatic Control 66(10), pp. 4604–4619.

Asynchronous optimization over graphs: linear convergence under error bound conditions

@ARTICLE{cannelli2020a,
   title = {Asynchronous optimization over graphs: linear convergence under error bound conditions},
   journal = {{IEEE} Transactions on Automatic Control},
   volume = {66},
   author = {Cannelli, L. and Facchinei, F. and Scutari, G. and Kungurtsev, V.},
   number = {10},
   pages = {4604--4619},
   year = {2020},
   doi = {10.1109/TAC.2020.3033490},
   url = {}
}
Download
Casanova, A., Miranda, E., Zaffalon, M. (2020). Social pooling of beliefs and values with desirability. Proceedings of the 33rd International Flairs Conference (FLAIRS-33).

Social pooling of beliefs and values with desirability

@ARTICLE{casanova2020a,
   title = {Social pooling of beliefs and values with desirability},
   journal = {Proceedings of the 33rd International Flairs Conference ({FLAIRS}-33)},
   author = {Casanova, A. and Miranda, E. and Zaffalon, M.},
   year = {2020},
   doi = {},
   url = {https://www.flairs-33.info/}
}
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Coletti, G., van der Gaag, L.C., Petturiti, D., Vantaggi, B. (2020). Detecting correlation between extreme probability events. International Journal of General Systems 49(1), pp. 64–87.

Detecting correlation between extreme probability events

@ARTICLE{Linda2019e,
   title = {Detecting correlation between extreme probability events},
   journal = {International Journal of General Systems },
   volume = {49},
   author = {Coletti, G. and van der Gaag, L.C. and Petturiti, D. and Vantaggi, B.},
   number = {1},
   pages = {64--87},
   year = {2020},
   doi = {10.1080/03081079.2019.1692005},
   url = {}
}
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Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2020). Probabilistic reconciliation of hierarchical forecast via Bayes’ rule. In Hutter, Frank, Kersting, Kristian, Lijffijt, Jefrey, Valera, Isabel (Eds), Joint European Conference on Machine Learning and Knowledge Discovery in Database (ECML- PKDD), Springer International Publishing, pp. 211–226.

Probabilistic reconciliation of hierarchical forecast via Bayes’ rule

@INPROCEEDINGS{corani2020a,
   title = {Probabilistic reconciliation of hierarchical forecast via {B}ayes’ rule},
   editor = {Hutter, Frank and Kersting, Kristian and Lijffijt, Jefrey and Valera, Isabel},
   publisher = {Springer International Publishing},
   booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Database ({ECML}- {PKDD})},
   author = {Corani, G. and Azzimonti, D. and Augusto, J.P.S.C. and Zaffalon, M.},
   pages = {211--226},
   year = {2020},
   doi = {10.1007/978-3-030-67664-3_13},
   url = {}
}
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Cózar, J., Cabañas, R., Salmerón, A., Masegosa, A.R. (2020). Inferpy: probabilistic modeling with deep neural networks made easy. Neurocomputing 415, pp. 408–410.

Inferpy: probabilistic modeling with deep neural networks made easy

@ARTICLE{cabanas2020c,
   title = {Inferpy: probabilistic modeling with deep neural networks made easy},
   journal = {Neurocomputing},
   volume = {415},
   author = {C\'ozar, J. and Caba\~nas, R. and Salmer\'on, A. and Masegosa, A.R.},
   pages = {408--410},
   year = {2020},
   doi = {10.1016/j.neucom.2020.07.117},
   url = {}
}
Download
Fisher, H., Gittoes, M., Evans, L., Bitchell, L., Mullen, R., Scutari, M. (2020). An interdisciplinary examination of stress and injury occurrence in athletes. Frontiers in Sports and Active Living 2, 595619.

An interdisciplinary examination of stress and injury occurrence in athletes

@ARTICLE{scutari20f,
   title = {An interdisciplinary examination of stress and injury occurrence in athletes},
   journal = {Frontiers in Sports and Active Living},
   volume = {2},
   author = {Fisher, H. and Gittoes, M. and Evans, L. and Bitchell, L. and Mullen, R. and Scutari, M.},
   pages = {595619},
   year = {2020},
   doi = {10.3389/fspor.2020.595619},
   url = {}
}
Download
Forgione, M., Piga, D. (2020). Model structures and fitting criteria for system identification with neural networks. In Proceedings of the 14th IEEE International Conference Application of Information and Communication Technologies (AICT 20).

Model structures and fitting criteria for system identification with neural networks

@INPROCEEDINGS{forgione2020b,
   title = {Model structures and fitting criteria for system identification with neural networks},
   booktitle = {Proceedings of the 14th {IEEE} International Conference Application of Information and Communication Technologies ({AICT} 20)},
   author = {Forgione, M. and Piga, D.},
   year = {2020},
   doi = {},
   url = {}
}
Download
Forgione, M., Piga, D., Bemporad, A. (2020). Efficient Calibration of Embedded MPC. In Proceedings of the 21st IFAC World Congress (IFAC 20) 53(2), pp. 5189–5194.

Efficient Calibration of Embedded MPC

@INPROCEEDINGS{forgione2020a,
   title = {Efficient {C}alibration of {E}mbedded {MPC}},
   journal = {{IFAC}-{PapersOnLine}},
   volume = {53},
   booktitle = {Proceedings of the 21st {IFAC} World Congress ({IFAC} 20)},
   author = {Forgione, M. and Piga, D. and Bemporad, A.},
   number = {2},
   pages = {5189--5194},
   year = {2020},
   doi = {10.1016/j.ifacol.2020.12.1188},
   url = {}
}
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van der Gaag, L.C., Bolt, J.H. (2020). Poset representations for sets of elementary triplets. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, JMLR.org, pp. 521–532.

Poset representations for sets of elementary triplets

@INPROCEEDINGS{Linda2020c,
   title = {Poset representations for sets of elementary triplets},
   publisher = {JMLR.org},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
   author = {van der Gaag, L.C. and Bolt, J.H.},
   pages = {521--532},
   year = {2020},
   doi = {},
   url = {https://pgm2020.cs.aau.dk/index.php/accepted-papers/}
}
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van der Gaag, L.C., Renooij, S., Facchini, A. (2020). Building causal interaction models by recursive unfolding. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020) 138, JMLR.org, pp. 509–520.

Building causal interaction models by recursive unfolding

@INPROCEEDINGS{Linda2020a,
   title = {Building causal interaction models by recursive unfolding},
   publisher = {JMLR.org},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
   author = {van der Gaag, L.C. and Renooij, S. and Facchini, A.},
   pages = {509--520},
   year = {2020},
   doi = {},
   url = {https://proceedings.mlr.press/v138/van-der-gaag20a.html}
}
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Geh, R., Mauá, D.D., Antonucci, A. (2020). Learning probabilistic sentential decision diagrams by sampling. In Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning (KMILE 2020), SBC, Porto Alegre, RS, Brasil, pp. 129–136.

Learning probabilistic sentential decision diagrams by sampling

@INPROCEEDINGS{antonucci2020c,
   title = {Learning probabilistic sentential decision diagrams by sampling},
   publisher = {SBC},
   address = {Porto Alegre, RS, Brasil},
   booktitle = {Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning ({KMILE} 2020)},
   author = {Geh, R. and Mau\'a, D.D. and Antonucci, A.},
   pages = {129--136},
   year = {2020},
   doi = {10.5753/kdmile.2020.11968},
   url = {}
}
Download
Huber, D., Cabañas, R., Antonucci, A., Zaffalon, M. (2020). CREMA: a Java library for credal network inference. In Jaeger, M., Nielsen, T.D. (Eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 613–616.

CREMA: a Java library for credal network inference

@INPROCEEDINGS{huber2020a,
   title = {{CREMA}: a {J}ava library for credal network inference},
   editor = {Jaeger, M. and Nielsen, T.D.},
   publisher = {PMLR},
   address = {Aalborg, Denmark},
   series = {Proceedings of Machine Learning Research},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
   author = {Huber, D. and Caba\~nas, R. and Antonucci, A. and Zaffalon, M.},
   pages = {613--616},
   year = {2020},
   doi = {},
   url = {https://pgm2020.cs.aau.dk}
}
Download
Kanjirangat, V., Mellace, S., Antonucci, A. (2020). Temporal embeddings and transformer models for narrative text understanding. In Third International Workshop on Narrative Extraction from Texts (Text2Story 20), 42nd European Conference on Information Retrieval (ECIR 20), ceur.

Temporal embeddings and transformer models for narrative text understanding

@INPROCEEDINGS{vani2020b,
   title = {Temporal embeddings and transformer models for narrative text understanding},
   publisher = {ceur},
   booktitle = {Third International Workshop on Narrative Extraction {f}rom Texts ({Text2Story} 20), 42nd European Conference on Information Retrieval ({ECIR} 20)},
   author = {Kanjirangat, V. and Mellace, S. and Antonucci, A.},
   year = {2020},
   doi = {},
   url = {http://ceur-ws.org/Vol-2593/paper9.pdf}
}
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Kanjirangat, V., Mitrovic, S., Antonucci, A., Rinaldi, F. (2020). SST-BERT at SemEval-2020 task 1: semantic shift tracing by clustering in BERT-based embedding spaces. In SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection.To appear In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain, pp. 214–221.

SST-BERT at SemEval-2020 task 1: semantic shift tracing by clustering in BERT-based embedding spaces

@INPROCEEDINGS{vani2020semeval,
   title = {{SST}-{BERT} at {SemEval}-2020 task 1: semantic shift tracing by clustering in {BERT}-based embedding spaces},
   booktitle = {{SemEval}-2020 Task 1: Unsupervised Lexical Semantic Change Detection.To {a}ppear In Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain},
   author = {Kanjirangat, V. and Mitrovic, S. and Antonucci, A. and Rinaldi, F.},
   pages = {214--221},
   year = {2020},
   doi = {10.18653/v1/2020.semeval-1.26},
   url = {}
}
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Kern, H., Corani, G., Huber, D., Vermes, N., Zaffalon, M., Varini, M., Wenzel, C., Fringer, A. (2020). Impact on place of death in cancer patients: a causal exploration in southern switzerland. BMC Palliative Care 19, 160.

Impact on place of death in cancer patients: a causal exploration in southern switzerland

@ARTICLE{Kern2020,
   title = {Impact on place of death in cancer patients: a causal exploration in southern switzerland},
   journal = {{BMC} Palliative Care},
   publisher = {Research Square},
   volume = {19},
   author = {Kern, H. and Corani, G. and Huber, D. and Vermes, N. and Zaffalon, M. and Varini, M. and Wenzel, C. and Fringer, A.},
   pages = {160},
   year = {2020},
   doi = {10.21203/rs.3.rs-29758/v3},
   url = {}
}
Download
Laurain, V., Tóth, R., Piga, D., Darwish, M.A.H. (2020). Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification. Automatica 115, 108914.

Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification

@ARTICLE{piga2020b,
   title = {Sparse {RKHS} estimation via globally convex optimization and its application in {LPV}-{IO} identification},
   journal = {Automatica},
   volume = {115},
   author = {Laurain, V. and T\'oth, R. and Piga, D. and Darwish, M.A.H.},
   pages = {108914},
   year = {2020},
   doi = {10.1016/j.automatica.2020.108914},
   url = {}
}
Download
Liew, B.X.W., Peolsson, A., Scutari, M., Löfgren, H., Wibault, J., r A Dedering, , Öberg, B., Zsigmond, P., Falla, D. (2020). Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks. European Journal of Pain 24(5), pp. 909–920.

Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks

@ARTICLE{scutari20a,
   title = {Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using bayesian networks},
   journal = {European Journal of Pain},
   volume = {24},
   author = {Liew, B.X.W. and Peolsson, A. and Scutari, M. and L\"ofgren, H. and Wibault, J. and r A Dedering, and \"Oberg, B. and Zsigmond, P. and Falla, D.},
   number = {5},
   pages = {909--920},
   year = {2020},
   doi = {10.1002/ejp.1537},
   url = {}
}
Download
Mangili, F., Broggini, D., Antonucci, A. (2020). Conversational recommender system by Bayesian methods. In Davis, Jesse, Tabia, Karim (Eds), Proceedings of the Fourteenth International Conference on Scalable Uncertainty Management (SUM 2020), Lecture Notes in Artificial Intelligence 12322, Springer, Cham, pp. 200–213.

Conversational recommender system by Bayesian methods

@INPROCEEDINGS{mangili2020b,
   title = {Conversational recommender system by {B}ayesian methods},
   editor = {Davis, Jesse and Tabia, Karim},
   publisher = {Springer, Cham},
   series = {Lecture Notes in Artificial Intelligence},
   volume = {12322},
   booktitle = {Proceedings of the Fourteenth International Conference on Scalable Uncertainty Management ({SUM} 2020)},
   author = {Mangili, F. and Broggini, D. and Antonucci, A.},
   pages = {200--213},
   year = {2020},
   doi = {10.1007/978-3-030-58449-8_14},
   url = {}
}
Download
Mangili, F., Broggini, D., Antonucci, A., Alberti, M., Cimasoni, L. (2020). A Bayesian approach to conversational recommendation systems. AAAI 2020 Workshop on Interactive and Conversational Recommendation Systems (WICRS-20).

A Bayesian approach to conversational recommendation systems

@ARTICLE{mangili2020a,
   title = {A {B}ayesian approach to conversational recommendation systems},
   journal = {{AAAI} 2020 Workshop on Interactive and Conversational Recommendation Systems ({WICRS}-20)},
   author = {Mangili, F. and Broggini, D. and Antonucci, A. and Alberti, M. and Cimasoni, L.},
   year = {2020},
   doi = {},
   url = {https://sites.google.com/view/wicrs2020}
}
Download
Mattei, L., Antonucci, A., Mauá, D.D., Facchini, A., Llerena, J.V. (2020). Tractable inference in credal sentential decision diagrams. International Journal of Approximate Reasoning 125, pp. 26–48.

Tractable inference in credal sentential decision diagrams

@ARTICLE{antonucci2020d,
   title = {Tractable inference in credal sentential decision diagrams},
   journal = {International Journal of Approximate Reasoning},
   volume = {125},
   author = {Mattei, L. and Antonucci, A. and Mau\'a, D.D. and Facchini, A. and Llerena, J.V.},
   pages = {26--48},
   year = {2020},
   doi = {10.1016/j.ijar.2020.06.005},
   url = {}
}
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Mauà, D.D., Ribeiro, H., Katague, G., Antonucci, A. (2020). Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. In Jaeger, M., Nielsen, T.D. (Eds), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), Proceedings of Machine Learning Research 138, PMLR, Aalborg, Denmark, pp. 293–304.

Two reformulation approaches to maximum-a-posteriori inference in sum-product networks

@INPROCEEDINGS{antonucci2020b,
   title = {Two reformulation approaches to maximum-a-posteriori inference in sum-product networks},
   editor = {Jaeger, M. and Nielsen, T.D.},
   publisher = {PMLR},
   address = {Aalborg, Denmark},
   series = {Proceedings of Machine Learning Research},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
   author = {Mau\`a, D.D. and Ribeiro, H. and Katague, G. and Antonucci, A.},
   pages = {293--304},
   year = {2020},
   doi = {},
   url = {https://proceedings.mlr.press/v138/maua20a.html}
}
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Mejari, M., Breschi, V., Naik, V.V., Piga, D. (2020). A bias-correction approach for the identification of piecewise affine output-error models. In 21st IFAC World Congress (IFAC 2020) 53(2), Berlin, Germany, pp. 1096–1101.

A bias-correction approach for the identification of piecewise affine output-error models

@INPROCEEDINGS{mejari2020a,
   title = {A bias-correction approach for the identification of piecewise affine output-error models},
   journal = {{IFAC}-{PapersOnLine}},
   address = {Berlin, Germany},
   volume = {53},
   booktitle = {21st {IFAC} World Congress ({IFAC} 2020)},
   author = {Mejari, M. and Breschi, V. and Naik, V.V. and Piga, D.},
   number = {2},
   pages = {1096--1101},
   year = {2020},
   doi = {10.1016/j.ifacol.2020.12.1307},
   url = {https://www.ifac2020.org}
}
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Mejari, M., Breschi, V., Piga, D. (2020). Recursive bias-correction method for identification of piecewise affine output-error models. IEEE Control Systems Letters 4, pp. 970–975.

Recursive bias-correction method for identification of piecewise affine output-error models

@ARTICLE{mejari2020b,
   title = {Recursive bias-correction method for identification of piecewise affine output-error models},
   journal = {{IEEE} Control Systems Letters},
   volume = {4},
   author = {Mejari, M. and Breschi, V. and Piga, D.},
   pages = {970--975},
   year = {2020},
   doi = {10.1109/LCSYS.2020.2998282},
   url = {}
}
Download
Mejari, M., Naik, V.V., Piga, D., Bemporad, A. (2020). Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming. International Journal of Robust and Nonlinear Control 30(15), pp. 5802–5819.

Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming

@ARTICLE{mejari2020c,
   title = {Identification of hybrid and linear parameter-varying models via piecewise affine regression using mixed integer programming},
   journal = {International Journal of Robust and Nonlinear Control},
   volume = {30},
   author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},
   number = {15},
   pages = {5802--5819},
   year = {2020},
   doi = {https://doi.org/10.1002/rnc.5198},
   url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.5198}
}
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Miranda, E., Zaffalon, M. (2020). Compatibility, desirability, and the running intersection property. Artificial Intelligence 283, 103724.

Compatibility, desirability, and the running intersection property

@ARTICLE{zaffalon2020a,
   title = {Compatibility, desirability, and the running intersection property},
   journal = {Artificial Intelligence},
   volume = {283},
   author = {Miranda, E., Zaffalon, M.},
   pages = {103724},
   year = {2020},
   doi = {10.1016/j.artint.2020.103274},
   url = {}
}
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Piga, D., Bemporad, A., Benavoli, A. (2020). Rao-Blackwellized sampling for batch and recursive Bayesian inference of piecewise affine models. Automatica 117, 109002.

Rao-Blackwellized sampling for batch and recursive Bayesian inference of piecewise affine models

@ARTICLE{piga2020a,
   title = {Rao-{B}lackwellized sampling for batch and recursive {B}ayesian inference of piecewise affine models},
   journal = {Automatica},
   volume = {117},
   author = {Piga, D. and Bemporad, A. and Benavoli, A.},
   pages = {109002},
   year = {2020},
   doi = {10.1016/j.automatica.2020.109002},
   url = {}
}
Download
Piga, D., Breschi, V., Bemporad, A. (2020). Estimation of jump box–jenkins models. Automatica 120, 109126.

Estimation of jump box–jenkins models

@ARTICLE{piga2020c,
   title = {Estimation of jump box--jenkins models},
   journal = {Automatica},
   volume = {120},
   author = {Piga, D. and Breschi, V. and Bemporad, A.},
   pages = {109126},
   year = {2020},
   doi = {10.1016/j.automatica.2020.109126},
   url = {http://www.sciencedirect.com/science/article/pii/S0005109820303241}
}
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Roveda, L., Bussolan, A., Braghin, F., Piga, D. (2020). 6D virtual sensor for wrench estimation in robotized interaction tasks exploiting extended Kalman filter. MDPI Machines 8(4), 67.

6D virtual sensor for wrench estimation in robotized interaction tasks exploiting extended Kalman filter

@ARTICLE{Roveda2020b,
   title = {{6D} virtual sensor for wrench estimation in robotized interaction tasks exploiting extended {K}alman filter},
   journal = {{MDPI} Machines},
   volume = {8},
   author = {Roveda, L. and Bussolan, A. and Braghin, F. and Piga, D.},
   number = {4},
   pages = {67},
   year = {2020},
   doi = {10.3390/machines8040067},
   url = {}
}
Download
Roveda, L., Castaman, N., Franceschi, P., Ghidoni, S., Pedrocchi, N. (2020). A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks. In IEEE International Conference on Robotics and Automation (ICRA) 2020, pp. 6819–6825.

A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks

@INPROCEEDINGS{Roveda2020f,
   title = {A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks},
   booktitle = {{IEEE} International Conference on Robotics and Automation ({ICRA}) 2020},
   author = {Roveda, L. and Castaman, N. and Franceschi, P. and Ghidoni, S. and Pedrocchi, N.},
   pages = {6819--6825},
   year = {2020},
   doi = {10.1109/ICRA40945.2020.9197141},
   url = {}
}
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Roveda, L., Forgione, M., Piga, D. (2020). Robot control parameters auto-tuning in trajectory tracking applications. Control Engineering Practice 101, 104488.

Robot control parameters auto-tuning in trajectory tracking applications

@ARTICLE{roveda2020a,
   title = {Robot control parameters auto-tuning in trajectory tracking applications},
   journal = {Control Engineering Practice},
   volume = {101},
   author = {Roveda, L. and Forgione, M. and Piga, D.},
   pages = {104488},
   year = {2020},
   doi = {10.1016/j.conengprac.2020.104488},
   url = {}
}
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Roveda, L., Forgione, M., Piga, D. (2020). One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications. In Ubiquitous Robots 2020, pp. 105–112.

One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications

@INPROCEEDINGS{Roveda2020h,
   title = {One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications},
   booktitle = {Ubiquitous Robots 2020},
   author = {Roveda, L. and Forgione, M. and Piga, D.},
   pages = {105--112},
   year = {2020},
   doi = {10.1109/UR49135.2020.9144761},
   url = {}
}
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Roveda, L., Magni, M., Cantoni, M., Piga, D., Bucca, G. (2020). Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization. Robotics and Autonomous Systems 136, 103711.

Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization

@ARTICLE{Roveda2020j,
   title = {Human--robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via {B}ayesian optimization},
   journal = {Robotics and Autonomous Systems},
   volume = {136},
   author = {Roveda, L. and Magni, M. and Cantoni, M. and Piga, D. and Bucca, G.},
   pages = {103711},
   year = {2020},
   doi = {10.1016/j.robot.2020.103711},
   url = {}
}
Download
Roveda, L., Magni, M., Cantoni, M., Piga, D., Bucca, G. (2020). Assembly task learning and optimization through Human’s demonstration and machine learning. In IEEE International Conference on Systems, Man, and Cybernetics, pp. 1852–1859.

Assembly task learning and optimization through Human’s demonstration and machine learning

@INPROCEEDINGS{Roveda2020k,
   title = {Assembly task learning and optimization through {H}uman’s demonstration and machine learning},
   booktitle = {{IEEE} International Conference on Systems, Man, and Cybernetics},
   author = {Roveda, L. and Magni, M. and Cantoni, M. and Piga, D. and Bucca, G.},
   pages = {1852--1859},
   year = {2020},
   doi = {10.1109/SMC42975.2020.9282911},
   url = {}
}
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Roveda, L., Maskani, J., Franceschi, P., Arash, A., Braghin, F., Molinari Tosatti, L., Pedrocchi, N. (2020). Model-based reinforcement learning variable impedance control for human-robot collaboration. Journal of Intelligent & Robotic Systems 100(2), pp. 417–433.

Model-based reinforcement learning variable impedance control for human-robot collaboration

@ARTICLE{Roveda2020c,
   title = {Model-based reinforcement learning variable impedance control for human-robot collaboration},
   journal = {Journal of Intelligent & Robotic Systems},
   publisher = {Springer},
   volume = {100},
   author = {Roveda, L. and Maskani, J. and Franceschi, P. and Arash, A. and Braghin, F. and Molinari Tosatti, L. and Pedrocchi, N.},
   number = {2},
   pages = {417--433},
   year = {2020},
   doi = {10.1007/s10846- 020-01183-3},
   url = {}
}
Download
Roveda, L., Piga, D. (2020). Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks. International Journal of Intelligent Robotics and Applications 4(4), pp. 507–519.

Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks

@ARTICLE{Roveda2020d,
   title = {Robust state dependent {R}iccati equation variable impedance control for robotic force-tracking tasks},
   journal = {International Journal of Intelligent Robotics and Applications},
   publisher = {Springer},
   volume = {4},
   author = {Roveda, L. and Piga, D.},
   number = {4},
   pages = {507--519},
   year = {2020},
   doi = {10.1007/s41315-020-00153-0},
   url = {}
}
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Roveda, L., Piga, D. (2020). Interaction force computation exploiting environment stiffness estimation for sensorless robot applications. In IEEE Metrology for Industry 4.0 and IoT 2020, pp. 360–363.

Interaction force computation exploiting environment stiffness estimation for sensorless robot applications

@INPROCEEDINGS{Roveda2020g,
   title = {Interaction force computation exploiting environment stiffness estimation for sensorless robot applications},
   booktitle = {{IEEE} Metrology for Industry 4.0 and {IoT} 2020},
   author = {Roveda, L. and Piga, D.},
   pages = {360--363},
   year = {2020},
   doi = {10.1109/MetroInd4.0IoT48571.2020.9138189},
   url = {}
}
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Roveda, L., Savani, L., Arlati, S., Dinon, T., Legnani, G., Molinari Tosatti, L. (2020). Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics. International Journal of Industrial Ergonomics 79, 102991.

Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics

@ARTICLE{Roveda2020e,
   title = {Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics},
   journal = {International Journal of Industrial Ergonomics},
   publisher = {Elsevier},
   volume = {79},
   author = {Roveda, L. and Savani, L. and Arlati, S. and Dinon, T. and Legnani, G. and Molinari Tosatti, L.},
   pages = {102991},
   year = {2020},
   doi = {10.1016/j.ergon.2020.102991},
   url = {}
}
Download
Ruggieri, A., Stranieri, F., Stella, F., Scutari, M. (2020). Hard and soft em in bayesian network learning from incomplete data. Algorithms 13(12), 329.

Hard and soft em in bayesian network learning from incomplete data

@ARTICLE{scutari20h,
   title = {Hard and soft em in bayesian network learning from incomplete data},
   journal = {Algorithms},
   volume = {13},
   author = {Ruggieri, A. and Stranieri, F. and Stella, F. and Scutari, M.},
   number = {12},
   pages = {329},
   year = {2020},
   doi = {10.3390/a13120329},
   url = {}
}
Download
Schürch, M., Azzimonti, D., Benavoli, A., Zaffalon, M. (2020). Recursive estimation for sparse gaussian process regression. Automatica 120, 109127.

Recursive estimation for sparse gaussian process regression

@ARTICLE{schurch2020a,
   title = {Recursive estimation for sparse gaussian process regression},
   journal = {Automatica},
   publisher = {Elsevier},
   volume = {120},
   author = {Sch\"urch, M. and Azzimonti, D. and Benavoli, A. and Zaffalon, M.},
   pages = {109127},
   year = {2020},
   doi = {10.1016/j.automatica.2020.109127},
   url = {}
}
Download
Shahid, A.A., Roveda, L., Piga, D., Braghin, F. (2020). Learning continuous control actions for robotic grasping with reinforcement learning. In IEEE International Conference on Systems, Man, and Cybernetics, pp. 4066–4072.

Learning continuous control actions for robotic grasping with reinforcement learning

@INPROCEEDINGS{Roveda2020l,
   title = {Learning continuous control actions for robotic grasping with reinforcement learning},
   booktitle = {{IEEE} International Conference on Systems, Man, and Cybernetics},
   author = {Shahid, A.A. and Roveda, L. and Piga, D. and Braghin, F.},
   pages = {4066--4072},
   year = {2020},
   doi = {10.1109/SMC42975.2020.9282951},
   url = {}
}
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Sheldrake, T.E., Caricchi, L., Scutari, M. (2020). Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs. Frontiers in Earth Sciences 8(127), pp. 1–14.

Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs

@ARTICLE{scutari20b,
   title = {Tectonic control on global variations in the record of large-magnitude explosive eruptions in volcanic arcs},
   journal = {Frontiers in Earth Sciences},
   volume = {8},
   author = {Sheldrake, T.E. and Caricchi, L. and Scutari, M.},
   number = {127},
   pages = {1--14},
   year = {2020},
   doi = {10.3389/feart.2020.00127},
   url = {}
}
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Sorgini, F., Airò Farulla, G., Lukic, N., Danilov, I., Roveda, L., Milivojevic, M., Babu Pulikottil, T., Carrozza, M.C., Prinetto, P., Tolio, T., Oddo, C.M., Petrovic, P., Bojovic, B. (2020). Tactile sensing with gesture-controlled collaborative robot. In 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT), pp. 364–368.

Tactile sensing with gesture-controlled collaborative robot

@INPROCEEDINGS{Roveda2020i,
   title = {Tactile sensing with gesture-controlled collaborative robot},
   booktitle = {2020 {IEEE} International Workshop on Metrology for Industry 4.0 & {IoT} ({MetroInd4}.0 & {IoT})},
   author = {Sorgini, F. and Air\`o Farulla, G. and Lukic, N. and Danilov, I. and Roveda, L. and Milivojevic, M. and Babu Pulikottil, T. and Carrozza, M.C. and Prinetto, P. and Tolio, T. and Oddo, C.M. and Petrovic, P. and Bojovic, B.},
   pages = {364--368},
   year = {2020},
   doi = {10.1109/MetroInd4.0IoT48571.2020.9138183},
   url = {}
}
Download
Szehr, O., Zarouf, R. (2020). Interpolation without commutants. Journal of Operator Theory 84(1), pp. 239–256.

Interpolation without commutants

@ARTICLE{szehr2020aa,
   title = {Interpolation without commutants},
   journal = {Journal of Operator Theory},
   volume = {84},
   author = {Szehr, O. and Zarouf, R.},
   number = {1},
   pages = {239--256},
   year = {2020},
   doi = {10.7900/jot.2019may21.2264},
   url = {}
}
Download
Volpetti, C., Kanjirangat, V., Antonucci, A. (2020). Temporal word embeddings for narrative understanding. In 12th International Conference on Machine Learning and Computing (ICMLC 2020) (5), ACM, pp. 68–72.

Temporal word embeddings for narrative understanding

@INPROCEEDINGS{supsi2020a,
   title = {Temporal word embeddings for narrative understanding},
   publisher = {ACM},
   booktitle = {12th International Conference on Machine Learning and Computing ({ICMLC} 2020)},
   author = {Volpetti, C. and Kanjirangat, V. and Antonucci, A.},
   number = {5},
   pages = {68--72},
   year = {2020},
   doi = {10.1145/3383972.3383988},
   url = {}
}
Download
Yoo, J., Kang, U., Scanagatta, M., Corani, G., Zaffalon, M. (2020). Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference. In Proceedings of International Conference on Web Search and Data Mining (WSDM '20) (9), pp. 708–16.

Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference

@INPROCEEDINGS{corani2019e,
   title = {Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference},
   booktitle = {Proceedings of International Conference on Web Search and Data Mining ({WSDM} '20)},
   author = {Yoo, J. and Kang, U. and Scanagatta, M. and Corani, G. and Zaffalon, M.},
   number = {9},
   pages = {708--16},
   year = {2020},
   doi = {10.1145/3336191.3371815},
   url = {}
}
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Zaffalon, M., Antonucci, A., Cabañas, R. (2020). Structural causal models are (solvable by) credal networks. In Jaeger, M., Nielsen, T. D. (Ed), Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), PMLR 138, JMLR.org, pp. 581–592.

Structural causal models are (solvable by) credal networks

@INPROCEEDINGS{zaffalon2020b,
   title = {Structural causal models are (solvable by) credal networks},
   editor = {Jaeger, M., Nielsen, T. D. },
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {138},
   booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},
   author = {Zaffalon, M. and Antonucci, A. and Cabañas, R.},
   pages = {581--592},
   year = {2020},
   doi = {},
   url = {http://proceedings.mlr.press/v138/zaffalon20a/zaffalon20a.pdf}
}
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top

2019

Antonucci, A. (2019). Reliable discretisation of deterministic equations in Bayesian networks. In Proceedings of the 32nd International Flairs Conference (FLAIRS-32), AAAI Press.

Reliable discretisation of deterministic equations in Bayesian networks

@INPROCEEDINGS{supsi2019c,
   title = {Reliable discretisation of deterministic equations in {B}ayesian networks},
   publisher = {AAAI Press},
   booktitle = {Proceedings of the 32nd International Flairs Conference ({FLAIRS}-32)},
   author = {Antonucci, A.},
   year = {2019},
   doi = {},
   url = {}
}
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Antonucci, A., Facchini, A., Mattei, L. (2019). Credal sentential decision diagrams. In Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19) 103, PMLR, pp. 14–22.

Credal sentential decision diagrams

@INPROCEEDINGS{supsi2019b,
   title = {Credal sentential decision diagrams},
   publisher = {PMLR},
   volume = {103},
   booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},
   author = {Antonucci, A. and Facchini, A. and Mattei, L.},
   pages = {14--22},
   year = {2019},
   doi = {},
   url = {https://proceedings.mlr.press/v103/antonucci19a.html}
}
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Arnone, E., Azzimonti, L., Nobile, F., Sangalli, L.M. (2019). Modeling spatially dependent functional data via regression with differential regularization. Journal of Multivariate Analysis 170, pp. 275–295.

Modeling spatially dependent functional data via regression with differential regularization

@ARTICLE{azzimonti2018a,
   title = {Modeling spatially dependent functional data via regression with differential regularization},
   journal = {Journal of Multivariate Analysis},
   volume = {170},
   author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},
   pages = {275--295},
   year = {2019},
   doi = {10.1016/j.jmva.2018.09.006},
   url = {}
}
Download
Azzimonti, L., Corani, G., Zaffalon, M. (2019). Hierarchical estimation of parameters in Bayesian networks. Computational Statistics and Data Analysis 137, pp. 67–91.

Hierarchical estimation of parameters in Bayesian networks

@ARTICLE{azzimonti2019a,
   title = {Hierarchical estimation of parameters in {B}ayesian networks},
   journal = {Computational Statistics and Data Analysis},
   volume = {137},
   author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},
   pages = {67--91},
   year = {2019},
   doi = {10.1016/j.csda.2019.02.004},
   url = {}
}
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Azzimonti, D., Ginsbourger, D., Chevalier, C., Bect, J., Richet, Y. (2019). Adaptive design of experiments for conservative estimation of excursion sets. Technometrics 63(1), pp. 13–26.

Adaptive design of experiments for conservative estimation of excursion sets

@ARTICLE{azzimontid2019c,
   title = {Adaptive design of experiments for conservative estimation of excursion sets},
   journal = {Technometrics},
   publisher = {Taylor & Francis},
   volume = {63},
   author = {Azzimonti, D. and Ginsbourger, D. and Chevalier, C. and Bect, J. and Richet, Y.},
   number = {1},
   pages = {13--26},
   year = {2019},
   doi = {10.1080/00401706.2019.1693427},
   url = {}
}
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Azzimonti, D., Ginsbourger, D., Rohmer, J., Idier, D. (2019). Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. Technometrics 61(4), pp. 474–493.

Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding

@ARTICLE{azzimontid2019a,
   title = {Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding},
   journal = {Technometrics},
   publisher = {Taylor & Francis},
   volume = {61},
   author = {Azzimonti, D. and Ginsbourger, D. and Rohmer, J. and Idier, D.},
   number = {4},
   pages = {474--493},
   year = {2019},
   doi = {10.1080/00401706.2018.1562987},
   url = {}
}
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Azzimonti, D., Rottondi, C., Tornatore, M. (2019). Using active learning to decrease probes for QoT estimation in optical networks. In , Optical Society of America, Th1H.1.

Using active learning to decrease probes for QoT estimation in optical networks

@INPROCEEDINGS{azzimontid2019b,
   title = {Using active learning to decrease probes for {QoT} estimation in optical networks},
   journal = {Optical Fiber Communication Conference ({OFC}) 2019},
   publisher = {Optical Society of America},
   author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},
   pages = {Th1H.1},
   year = {2019},
   doi = {10.1364/OFC.2019.Th1H.1},
   url = {}
}
Download
Benavoli, A., Facchini, A., Zaffalon, M. (2019). Bernstein's socks, polynomial-time provable coherence and entanglement. In De Bock, J., de Campos, C., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), ISIPTA '19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications, PMLR 103, JMLR.org, pp. 23–31.

Bernstein's socks, polynomial-time provable coherence and entanglement

@INPROCEEDINGS{zaffalon2019b,
   title = {Bernstein's socks, polynomial-time provable coherence and entanglement},
   editor = {De Bock, J. and de Campos, C. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {103},
   booktitle = {{ISIPTA };'19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications},
   author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
   pages = {23--31},
   year = {2019},
   doi = {},
   url = {https://proceedings.mlr.press/v103/benavoli19a.html}
}
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Benavoli, A., Facchini, A., Piga, D., Zaffalon, M. (2019). Sum-of-squares for bounded rationality. International Journal of Approximate Reasoning 105, pp. 130–152.

Sum-of-squares for bounded rationality

@ARTICLE{benavoli2019a,
   title = {Sum-of-squares for bounded rationality},
   journal = {International Journal of Approximate Reasoning},
   volume = {105},
   author = {Benavoli, A., Facchini, A., Piga, D., Zaffalon, M.},
   pages = {130--152},
   year = {2019},
   doi = {10.1016/j.ijar.2018.11.012},
   url = {}
}
Download
Bolt, J.H., van der Gaag, L.C. (2019). On minimum elementary-triplet bases for independence relations. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19), PMLR 103, JMLR.org, pp. 32–37.

On minimum elementary-triplet bases for independence relations

@INPROCEEDINGS{Linda2019c,
   title = {On minimum elementary-triplet bases for independence relations},
   editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {103},
   booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},
   author = {Bolt, J.H. and van der Gaag, L.C.},
   pages = {32--37},
   year = {2019},
   doi = {},
   url = {https://proceedings.mlr.press/v103/bolt19a.html}
}
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Breschi, V., Piga, D., Bemporad, A. (2019). Online end-use energy disaggregation via jump linear models. Control Engineering Practice 89, pp. 30–42.

Online end-use energy disaggregation via jump linear models

@ARTICLE{piga2019b,
   title = {Online end-use energy disaggregation via jump linear models},
   journal = {Control Engineering Practice},
   volume = {89},
   author = {Breschi, V. and Piga, D. and Bemporad, A.},
   pages = {30--42},
   year = {2019},
   doi = {10.1016/j.conengprac.2019.05.011},
   url = {}
}
Download
Bucher, D., Mangili, F., Cellina, F., Bonesana, C., Jonietz, D., Raubal, M. (2019). From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behaviour and Society 14, pp. 43–56.

From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors

@ARTICLE{mangili2019a,
   title = { From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors},
   journal = {Travel Behaviour and Society},
   volume = {14},
   author = {Bucher, D. and Mangili, F. and Cellina, F. and Bonesana, C. and Jonietz, D. and Raubal, M.},
   pages = {43--56},
   year = {2019},
   doi = {10.1016/j.tbs.2018.09.005},
   url = {}
}
Download
Carollo, V., Piga, D., Borri, C., Paggi, M. (2019). Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics. Engineering Fracture Mechanics 205, pp. 439–454.

Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics

@ARTICLE{piga2019d,
   title = {Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics},
   journal = {Engineering Fracture Mechanics},
   volume = {205},
   author = {Carollo, V. and Piga, D. and Borri, C. and Paggi, M.},
   pages = {439--454},
   year = {2019},
   doi = {10.1016/j.engfracmech.2018.11.014},
   url = {}
}
Download
Cellina, F., Bucher, D., Mangili, F., Simão, J.V., Rudel, R., Raubal, M. (2019). A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt. Sustainability 11(9), 2674.

A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt

@ARTICLE{mangili2019b,
   title = {A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt},
   journal = {Sustainability},
   editor = {mdpi},
   volume = {11},
   author = {Cellina, F. and Bucher, D. and Mangili, F. and Sim\~ao, J.V. and Rudel, R. and Raubal, M.},
   number = {9},
   pages = {2674},
   year = {2019},
   doi = {10.3390/su11092674},
   url = {}
}
Download
Colic, N., Rinaldi, F. (2019). Improving spaCy dependency annotation and PoS tagging web service using independent NER services. Genomics Inform 17(2), e21.

Improving spaCy dependency annotation and PoS tagging web service using independent NER services

@ARTICLE{rinaldi2019c,
   title = {Improving {spaCy} dependency annotation and {PoS} tagging web service using independent {NER} services},
   journal = {Genomics Inform},
   volume = {17},
   author = {Colic, N. and Rinaldi, F.},
   number = {2},
   pages = {e21},
   year = {2019},
   doi = {10.5808/GI.2019.17.2.e21},
   url = {}
}
Download
Correia, A.H.C., de Campos, C.P., van der Gaag, L.C. (2019). An experimental study of prior dependence in Bayesian network structure learning. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19), PMLR 103, JMLR.org, pp. 78–81.

An experimental study of prior dependence in Bayesian network structure learning

@INPROCEEDINGS{Linda2910d,
   title = {An experimental study of prior dependence in {B}ayesian network structure learning},
   editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
   publisher = {JMLR.org},
   series = {PMLR},
   volume = {103},
   booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},
   author = {Correia, A.H.C. and de Campos, C.P. and van der Gaag, L.C.},
   pages = {78--81},
   year = {2019},
   doi = {},
   url = {https://proceedings.mlr.press/v103/correia19a.html}
}
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Ellendorff, T., Furrer, L., Colic, N., Aepli, N., Rinaldi, F. (2019). Approaching SMM4H with merged models and multi-task learning. In Proceedings of the Fourth Social Media Mining for Health Applications (#smm4h) Workshop & Shared Task, Association for Computational Linguistics, pp. 58–61.

Approaching SMM4H with merged models and multi-task learning

@INPROCEEDINGS{rinaldi2019g,
   title = {Approaching {SMM4H} with merged models and multi-task learning},
   publisher = {Association for Computational Linguistics},
   booktitle = {Proceedings of the Fourth Social Media Mining for Health Applications (\#smm4h) Workshop & Shared Task},
   author = {Ellendorff, T. and Furrer, L. and Colic, N. and Aepli, N. and Rinaldi, F.},
   pages = {58--61},
   year = {2019},
   doi = {10.18653/v1/W19-3208},
   url = {https://www.aclweb.org/anthology/W19-3208}
}
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Furrer, L., Cornelius, J., Rinaldi, F. (2019). UZH@CRAFT-ST: a sequence-labeling approach to concept recognition. In Proceedings of the 5th Workshop on Bionlp Open Shared Tasks, Association for Computational Linguistics, pp. 185–195.

UZH@CRAFT-ST: a sequence-labeling approach to concept recognition

@INPROCEEDINGS{rinaldi2019h,
   title = {{UZH@CRAFT}-{ST}: a sequence-labeling approach to concept recognition},
   publisher = {Association for Computational Linguistics},
   booktitle = {Proceedings of the 5th Workshop on Bionlp Open Shared Tasks},
   author = {Furrer, L. and Cornelius, J. and Rinaldi, F.},
   pages = {185--195},
   year = {2019},
   doi = {10.18653/v1/D19-5726},
   url = {}
}
Download
Furrer, L., Jancso, A., Colic, N., Rinaldi, F. (2019). Oger++: hybrid multi-type entity recognition. Journal of Cheminformatics 11(1), 7.

Oger++: hybrid multi-type entity recognition

@ARTICLE{rinaldi2019d,
   title = {Oger++: hybrid multi-type entity recognition},
   journal = {Journal of Cheminformatics},
   publisher = {BioMed Central},
   volume = {11},
   author = {Furrer, L. and Jancso, A. and Colic, N. and Rinaldi, F.},
   number = {1},
   pages = {7},
   year = {2019},
   doi = {10.1186/s13321-018-0326-3},
   url = {https://doi.org/10.5167/uzh-162875}
}
Download
Kanjirangat, V., Antonucci, A. (2019). NOVEL2GRAPH: Visual summaries of narrative text enhanced by machine learning. Proceedings of the Text2StoryIR'19 Workshop, Cologne, Germany, 14-April-2019, pp. 29–37.

NOVEL2GRAPH: Visual summaries of narrative text enhanced by machine learning

@ARTICLE{vani2019a,
   title = {{NOVEL2GRAPH}: {V}isual summaries of narrative text enhanced by machine learning},
   journal = {Proceedings of the {Text2StoryIR'19} Workshop, Cologne, Germany, 14-April-2019},
   editor = {A. Jorge, R. Campos, A. Jatowt, S. Bhatia},
   publisher = {ceur},
   author = {Kanjirangat, V. and Antonucci, A.},
   pages = {29--37},
   year = {2019},
   doi = {},
   url = {http://ceur-ws.org/Vol-2342/paper4.pdf}
}
Download
Kanjirangat, V., Oita, M., Oezdemir-Zaech, F. (2019). Semantically corroborating neural attention for biomedical question answering. In Machine Learning and Knowledge Discovery in Databases, Springer, Lecture Notes in Computer Science, pp. 670–685.

Semantically corroborating neural attention for biomedical question answering

@INPROCEEDINGS{supsi2019d,
   title = {Semantically corroborating neural attention for biomedical question answering},
   publisher = {Springer, Lecture Notes in Computer Science},
   booktitle = {Machine Learning and Knowledge Discovery in Databases},
   author = {Kanjirangat, V. and Oita, M. and Oezdemir-Zaech, F.},
   pages = {670--685},
   year = {2019},
   doi = {10.1007/978-3-030-43887-6_60},
   url = {}
}
Download
Kim, J.D., Cohen, K.B., Collier, N., Lu, Z., Rinaldi, F. (2019). Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining. Genomics Inform 17(2), e12.

Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining

@ARTICLE{rinaldi2019b,
   title = {Introduction to {BLAH5} special issue: recent progress on interoperability of biomedical text mining},
   journal = {Genomics Inform},
   volume = {17},
   author = {Kim, J.D. and Cohen, K.B. and Collier, N. and Lu, Z. and Rinaldi, F.},
   number = {2},
   pages = {e12},
   year = {2019},
   doi = {10.5808/GI.2019.17.2.e12},
   url = {}
}
Download
Mattei, L., Soares, D.L., Antonucci, A., Mauà, D.D., Facchini, A. (2019). Exploring the space of probabilistic sentential decision diagrams. In Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning.

Exploring the space of probabilistic sentential decision diagrams

@INPROCEEDINGS{supsi2019a,
   title = {Exploring the space of probabilistic sentential decision diagrams},
   booktitle = {Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning},
   author = {Mattei, L. and Soares, D.L. and Antonucci, A. and Mau\`a, D.D. and Facchini, A.},
   year = {2019},
   doi = {},
   url = {https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view}
}
Download
Mauri, A., Lettori, J., Fusi, G., Fausti, D., Mor, M., Braghin, F., Legnani, G., Roveda, L. (2019). Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks. MDPI Robotics.

Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks

@ARTICLE{Roveda2019b,
   title = {Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks},
   journal = {{MDPI} Robotics},
   author = {Mauri, A. and Lettori, J. and Fusi, G. and Fausti, D. and Mor, M. and Braghin, F. and Legnani, G. and Roveda, L.},
   year = {2019},
   doi = {10.3390/robotics8030065},
   url = {}
}
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Mejari, M., Petreczky, M. (2019). Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.. IFAC-PapersOnLine 52(28), pp. 13–19.

Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.

@ARTICLE{mejari2019a,
   title = {Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.},
   journal = {{IFAC}-{PapersOnLine}},
   volume = {52},
   author = {Mejari, M. and Petreczky, M.},
   number = {28},
   pages = {13--19},
   year = {2019},
   doi = {https://doi.org/10.1016/j.ifacol.2019.12.340},
   url = {https://www.sciencedirect.com/science/article/pii/S2405896319322402}
}
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Mejari, M., Petreczky, M. (2019). Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach. In 2019 Ieee 58th Conference on Decision and Control (cdc), pp. 3805–3810.

Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach

@INPROCEEDINGS{mejari2019b,
   title = {Consistent and computationally efficient estimation for stochastic lpv state-space models: realization based approach},
   booktitle = {2019 Ieee 58th Conference on Decision and Control ({c}dc)},
   author = {Mejari, M. and Petreczky, M.},
   pages = {3805--3810},
   year = {2019},
   doi = {10.1109/CDC40024.2019.9030164},
   url = {}
}
Download
Mejari, M., Piga, D., Toth, R., Bemporad, A. (2019). Kernelized identification of linear parameter-varying models with linear fractional representation. In 2019 European Control Conference (ecc), Naples, Italy.

Kernelized identification of linear parameter-varying models with linear fractional representation

@INPROCEEDINGS{piga2019e,
   title = {Kernelized identification of linear parameter-varying models with linear fractional representation},
   address = {Naples, Italy},
   booktitle = {2019 European Control Conference ({e}cc)},
   author = {Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.},
   year = {2019},
   doi = {10.23919/ECC.2019.8796150},
   url = {}
}
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Miranda, E., Zaffalon, M. (2019). Compatibility, coherence and the RIP. In Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Ed), Uncertainty Modelling in Data Science, Advances in Intelligent Systems and Computing 832, Springer, pp. 166–174.

Compatibility, coherence and the RIP

@INCOLLECTION{zaffalon2018a,
   title = {Compatibility, coherence and the {RIP}},
   editor = {Destercke, S.,Denoeux, T., Gil, M. A., Grzegorzewski, P., Hryniewicz, O.},
   publisher = {Springer},
   series = {Advances in Intelligent Systems and Computing},
   volume = {832},
   booktitle = {Uncertainty Modelling in Data Science},
   author = {Miranda, E. and Zaffalon, M.},
   pages = {166--174},
   year = {2019},
   doi = {10.1007/978-3-319-97547-4_22},
   url = {}
}
Download
Musumeci, F., Rottondi, C.E.M., Corani, G., Shahkarami, S., Cugini, F., Tornatore, M. (2019). A tutorial on machine learning for failure management in optical networks. Journal of Lightwave Technology 37(16), pp. 4125–4139.

A tutorial on machine learning for failure management in optical networks

@ARTICLE{corani2019b,
   title = {A tutorial on machine learning for failure management in optical networks},
   journal = {Journal of Lightwave Technology},
   volume = {37},
   author = {Musumeci, F. and Rottondi, C.E.M. and Corani, G. and Shahkarami, S. and Cugini, F. and Tornatore, M.},
   number = {16},
   pages = {4125--4139},
   year = {2019},
   doi = {10.1109/JLT.2019.2922586},
   url = {}
}
Download
Oita, M. (2019). Reverse engineering creativity into interpretable neural networks. In Future of Information and Communications, Lecture Notes in Networks and Systems 70, pp. 235–247.

Reverse engineering creativity into interpretable neural networks

@INPROCEEDINGS{oita2019innGenuity,
   title = {Reverse engineering creativity into interpretable neural networks},
   series = {Lecture Notes in Networks and Systems},
   volume = {70},
   booktitle = {Future of Information and Communications},
   author = {Oita, M.},
   pages = {235--247},
   year = {2019},
   doi = {10.1007/978-3-030-12385-7_19},
   url = {}
}
Download
Oita, M. (2019). Incremental alignment of metaphoric language model for poetry composition. In Intelligent Computing, Springer, "Advances in Intelligent Systems and Computing", pp. 834–845.

Incremental alignment of metaphoric language model for poetry composition

@INPROCEEDINGS{oita2019poetryComposition,
   title = {Incremental alignment of metaphoric language model for poetry composition},
   publisher = {Springer, "Advances in Intelligent Systems and Computing"},
   booktitle = {Intelligent Computing},
   author = {Oita, M.},
   pages = {834--845},
   year = {2019},
   doi = {10.1007/978-3-030-22871-2_59},
   url = {}
}
Download
Piga, D. (2019). Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators. International Journal of Control 93(10), pp. 2378–2391.

Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators

@ARTICLE{piga2019c,
   title = {Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators},
   journal = {International Journal of Control},
   publisher = {Taylor & Francis},
   volume = {93},
   author = {Piga, D.},
   number = {10},
   pages = {2378--2391},
   year = {2019},
   doi = {10.1080/00207179.2018.1557348},
   url = {}
}
Download
Piga, D., Benavoli, A. (2019). Semialgebraic outer approximations for set-valued nonlinear filtering. In 2019 European Control Conference (ECC), Naples, Italy.

Semialgebraic outer approximations for set-valued nonlinear filtering

@INPROCEEDINGS{piga2019f,
   title = {Semialgebraic outer approximations for set-valued nonlinear filtering},
   address = {Naples, Italy},
   booktitle = {2019 European Control Conference ({ECC})},
   author = {Piga, D. and Benavoli, A.},
   year = {2019},
   doi = {10.23919/ECC.2019.8795731},
   url = {}
}
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Piga, D., Forgione, M., Formentin, S., Bemporad, A. (2019). Performance-oriented model learning for data-driven MPC design. IEEE Control Systems Letters 3(3), pp. 577–582.

Performance-oriented model learning for data-driven MPC design

@ARTICLE{piga2019a,
   title = {Performance-oriented model learning for data-driven {MPC} design},
   journal = {{IEEE} Control Systems Letters},
   volume = {3},
   author = {Piga, D. and Forgione, M. and Formentin, S. and Bemporad, A.},
   number = {3},
   pages = {577--582},
   year = {2019},
   doi = {10.1109/LCSYS.2019.2913347},
   url = {}
}
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Renooij, S., van der Gaag, L.C. (2019). The hidden elegance of causal interaction models. In Ben Amor, N., Quost, B., Theobald, M (Eds), 13th International Conference on Scalable Uncertainty Management (SUM '19), Lecture Notes in Artificial Intelligence 11940, Springer, pp. 38–51.

The hidden elegance of causal interaction models

@INPROCEEDINGS{linda2019a,
   title = {The hidden elegance of causal interaction models},
   editor = {Ben Amor, N. and Quost, B. and Theobald, M},
   publisher = {Springer},
   series = {Lecture Notes in Artificial Intelligence},
   volume = {11940},
   booktitle = {13th International Conference on Scalable Uncertainty Management ({SUM} '19)},
   author = {Renooij, S. and van der Gaag, L.C.},
   pages = {38--51},
   year = {2019},
   doi = {10.1007/978-3-030-35514-2_4},
   url = {}
}
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Renooij, S., van der Gaag, L.C., Leray, Ph. (2019). On intercausal interactions in probabilistic relational models. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA '19), Proceedings of Machine Learning Research 103, pp. 327–329.

On intercausal interactions in probabilistic relational models

@INPROCEEDINGS{Linda2019b,
   title = {On intercausal interactions in probabilistic relational models},
   editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},
   series = {Proceedings of Machine Learning Research},
   volume = {103},
   booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications ({ISIPTA} '19)},
   author = {Renooij, S. and van der Gaag, L.C. and Leray, Ph.},
   pages = {327--329},
   year = {2019},
   doi = {},
   url = {https://proceedings.mlr.press/v103/renooij19a.html}
}
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Rodriguez-Esteban, R., Vishnyakova, D., Rinaldi, F. (2019). Revisiting the decay of scientific email addresses. bioRxiv.

Revisiting the decay of scientific email addresses

@ARTICLE{rinaldi2019j,
   title = {Revisiting the decay of scientific email addresses},
   journal = {{bioRxiv}},
   publisher = {Cold Spring Harbor Laboratory},
   author = {Rodriguez-Esteban, R. and Vishnyakova, D. and Rinaldi, F.},
   year = {2019},
   doi = {10.1101/633255},
   url = {https://www.biorxiv.org/content/early/2019/05/12/633255}
}
Download
Roveda, L., Haghshenas, S., Caimmi, M., Pedrocchi, N., Molinari Tosatti, L. (2019). Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules. Frontiers in Robotics and AI 6, 75.

Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules

@ARTICLE{Roveda2019a,
   title = {Assisting operators in heavy industrial tasks: on the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules},
   journal = {Frontiers in Robotics and {AI}},
   volume = {6},
   author = {Roveda, L. and Haghshenas, S. and Caimmi, M. and Pedrocchi, N. and Molinari Tosatti, L.},
   pages = {75},
   year = {2019},
   doi = {10.3389/frobt.2019.00075},
   url = {}
}
Download
Salani, M., Corbellini, G., Corani, G. (2019). Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects. Computers & Operations Research 108, pp. 112–120.

Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects

@ARTICLE{corani2019a,
   title = {Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects},
   journal = {Computers & Operations Research},
   volume = {108},
   author = {Salani, M. and Corbellini, G. and Corani, G.},
   pages = {112--120},
   year = {2019},
   doi = {10.1016/j.cor.2019.04.009},
   url = {}
}
Download
Sechidis, K., Azzimonti, L., Pocock, A., Corani, G., Weatherall, J., Brown, G. (2019). Efficient feature selection using shrinkage estimators. Machine Learning 108(8), pp. 1261–1286.

Efficient feature selection using shrinkage estimators

@ARTICLE{azzimonti2019b,
   title = {Efficient feature selection using shrinkage estimators},
   journal = {Machine Learning},
   volume = {108},
   author = {Sechidis, K. and Azzimonti, L. and Pocock, A. and Corani, G. and Weatherall, J. and Brown, G.},
   number = {8},
   pages = {1261--1286},
   year = {2019},
   doi = {10.1007/s10994-019-05795-1},
   url = {}
}
Download
Sheikhalishahi, S., Miotto, R., Dudley, J.T., Lavelli, A., Rinaldi, F., Osmani, V. (2019). Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inform 7(2), e12239.

Natural language processing of clinical notes on chronic diseases: systematic review

@ARTICLE{rinaldi2019f,
   title = {Natural language processing of clinical notes on chronic diseases: systematic review},
   journal = {{JMIR} Med Inform},
   volume = {7},
   author = {Sheikhalishahi, S. and Miotto, R. and Dudley, J.T. and Lavelli, A. and Rinaldi, F. and Osmani, V.},
   number = {2},
   pages = {e12239},
   year = {2019},
   doi = {10.2196/12239},
   url = {http://medinform.jmir.org/2019/2/e12239/}
}
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Vishnyakova, D., Rodriguez-Esteban, R., Rinaldi, F. (2019). A new approach and gold standard toward author disambiguation in MEDLINE. J Am Med Inform Assoc 26(10), pp. 1037–1045.

A new approach and gold standard toward author disambiguation in MEDLINE

@ARTICLE{rinaldi2019a,
   title = {A new approach and gold standard toward author disambiguation in {MEDLINE}},
   journal = {J Am Med Inform Assoc},
   volume = {26},
   author = {Vishnyakova, D. and Rodriguez-Esteban, R. and Rinaldi, F.},
   number = {10},
   pages = {1037--1045},
   year = {2019},
   doi = {10.1093/jamia/ocz028},
   url = {}
}
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top

2018

Antonucci, A., Facchini, A. (2018). A credal extension of independent choice logic. In Proceedings of the 12th International Conference on Scalable Uncertainty Management (SUM 2018), pp. 35–49.

A credal extension of independent choice logic

@INPROCEEDINGS{antonucci2018c,
   title = {A credal extension of independent choice logic},
   booktitle = {Proceedings of the 12th International Conference on Scalable Uncertainty Management ({SUM} 2018)},
   author = {Antonucci, A. and Facchini, A.},
   pages = {35--49},
   year = {2018},
   doi = {10.1007/978-3-030-00461-3_3},
   url = {https://arxiv.org/abs/1806.08298}
}
Download
Antonucci, A., Facchini, A. (2018). Set-valued probabilistic sentential decision diagrams. In Proceedings of the 5th Workshop on Probabilistic Logic Programming, pp. 3–8.

Set-valued probabilistic sentential decision diagrams

@INPROCEEDINGS{antonucci2018d,
   title = {Set-valued probabilistic sentential decision diagrams},
   booktitle = {Proceedings of the 5th Workshop on Probabilistic Logic Programming},
   author = {Antonucci, A. and Facchini, A.},
   pages = {3--8},
   year = {2018},
   doi = {},
   url = {https://ceur-ws.org/Vol-2219/}
}
Download
Bemporad, A., Breschi, V., Piga, D., Boyd, S. (2018). Fitting jump models. Automatica 96, pp. 11–21.

Fitting jump models

@ARTICLE{piga2018e,
   title = {Fitting jump models},
   journal = {Automatica},
   volume = {96},
   author = {Bemporad, A. and Breschi, V. and Piga, D. and Boyd, S.},
   pages = {11--21},
   year = {2018},
   doi = {10.1016/j.automatica.2018.06.022},
   url = {}
}
Download
Breschi, V., Bemporad, A., Piga, D., Boyd, S. (2018). Prediction error methods in learning jump ARMAX models. In 2018 IEEE Conference on Decision and Control (cdc), pp. 2247–2252.

Prediction error methods in learning jump ARMAX models

@INPROCEEDINGS{piga2018i,
   title = {Prediction error methods in learning jump {ARMAX} models},
   booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},
   author = {Breschi, V. and Bemporad, A. and Piga, D. and Boyd, S.},
   pages = {2247--2252},
   year = {2018},
   doi = {10.1109/CDC.2018.8619819},
   url = {}
}
Download
Breschi, V., Piga, D., Bemporad, A. (2018). Kalman filtering for energy disaggregation. In Proc. of the 1st IFAC Workshop on Integrated Assessment Modelling for Environmental Systems 51(5), pp. 108–113.

Kalman filtering for energy disaggregation

@INPROCEEDINGS{piga2018b,
   title = {Kalman filtering for energy disaggregation},
   journal = {{IFAC}-{PapersOnLine}},
   volume = {51},
   booktitle = {Proc. {o}f the 1st {IFAC} Workshop on Integrated Assessment Modelling for Environmental Systems},
   author = {Breschi, V. and Piga, D. and Bemporad, A.},
   number = {5},
   pages = {108--113},
   year = {2018},
   doi = {10.1016/j.ifacol.2018.06.219},
   url = {}
}
Download
Breschi, V., Piga, D., Bemporad, A. (2018). Jump model learning and filtering for energy end-use disaggregation. In Proc. of the 18th IFAC Symposium on System Identification 51(15), pp. 275–280.

Jump model learning and filtering for energy end-use disaggregation

@INPROCEEDINGS{piga2018f,
   title = {Jump model learning and filtering for energy end-use disaggregation},
   volume = {51},
   booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},
   author = {Breschi, V. and Piga, D. and Bemporad, A.},
   number = {15},
   pages = {275--280},
   year = {2018},
   doi = {10.1016/j.ifacol.2018.09.147},
   url = {}
}
Download
de Campos, C.P., Scanagatta, M., Corani, G., Zaffalon, M. (2018). Entropy-based pruning for learning Bayesian networks using BIC. Artificial Intelligence 260, pp. 42–50.

Entropy-based pruning for learning Bayesian networks using BIC

@ARTICLE{deCampos2018a,
   title = {Entropy-based pruning for learning {B}ayesian networks using {BIC}},
   journal = {Artificial Intelligence},
   volume = {260},
   author = {de Campos, C.P. and Scanagatta, M. and Corani, G. and Zaffalon, M.},
   pages = {42--50},
   year = {2018},
   doi = {10.1016/j.artint.2018.04.002},
   url = {}
}
Download
Giusti, A., Huber, D., Gambardella, L.M. (2018). Introducing Machine Learning Concepts by Training a Neural Network to Recognize Hand Gestures. In Proc. of AAAI Symposium On Educational Advances In Artificial Intelligence 32(1).

Introducing Machine Learning Concepts by Training a Neural Network to Recognize Hand Gestures

@INPROCEEDINGS{huber2018a,
   title = {Introducing {M}achine {L}earning {C}oncepts by {T}raining a {N}eural {N}etwork to {R}ecognize {H}and {G}estures},
   journal = {Proceedings of the {AAAI} Conference on Artificial Intelligence},
   volume = {32},
   booktitle = {Proc. {o}f {AAAI} Symposium On Educational Advances In Artificial Intelligence},
   author = {Giusti, A. and Huber, D. and Gambardella, L.M.},
   number = {1},
   year = {2018},
   doi = {10.1609/aaai.v32i1.11400},
   url = {}
}
Download
Kern, H., Corani, G., Huber, D., Vermes, N., Zaffalon, M. (2018). What interplay of factors influences the place of death in cancer patients? an innovative probabilistic approach sheds light on a well-known question. Journal of Pain and Symptom Management 56(6), e25.

What interplay of factors influences the place of death in cancer patients? an innovative probabilistic approach sheds light on a well-known question

@ARTICLE{kern2018a,
   title = {What interplay of factors influences the place of death in cancer patients? {a}n innovative probabilistic approach sheds light on a well-known question},
   journal = {Journal of Pain and Symptom Management},
   publisher = {Elsevier},
   volume = {56},
   booktitle = {Journal of Pain and Symptom Management},
   author = {Kern, H. and Corani, G. and Huber, D. and Vermes, N. and Zaffalon, M.},
   number = {6},
   pages = {e25},
   year = {2018},
   doi = {10.1016/j.jpainsymman.2018.10.016},
   url = {}
}
Download
Marchetti, S., Antonucci, A. (2018). Reliable uncertain evidence modeling in Bayesian networks by credal networks. In Proceedings of the 31st International Flairs Conference (FLAIRS-31), AAAI Press, pp. 513–518.

Reliable uncertain evidence modeling in Bayesian networks by credal networks

@INPROCEEDINGS{antonucci2018a,
   title = {Reliable uncertain evidence modeling in {B}ayesian networks by credal networks},
   publisher = {AAAI Press},
   booktitle = {Proceedings of the 31st International Flairs Conference ({FLAIRS}-31)},
   author = {Marchetti, S. and Antonucci, A.},
   pages = {513--518},
   year = {2018},
   doi = {},
   url = {}
}
Download
Marchetti, S., Antonucci, A. (2018). Imaginary kinematics. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, AUAI Press, pp. 104–113.

Imaginary kinematics

@INPROCEEDINGS{antonucci2018b,
   title = {Imaginary kinematics},
   publisher = {AUAI Press},
   booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence},
   author = {Marchetti, S. and Antonucci, A.},
   pages = {104--113},
   year = {2018},
   doi = {},
   url = {https://www.auai.org/uai2018/accepted.php#top}
}
Download
Mejari, M., Naik, V.V., Piga, D., Bemporad, A. (2018). Regularized moving-horizon PWA regression for LPV system identification. In Proc. of the 18th IFAC Symposium on System Identification 51(15), pp. 1092–1097.

Regularized moving-horizon PWA regression for LPV system identification

@INPROCEEDINGS{piga2018d,
   title = {Regularized moving-horizon {PWA} regression for {LPV} system identification},
   volume = {51},
   booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},
   author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},
   number = {15},
   pages = {1092--1097},
   year = {2018},
   doi = {10.1016/j.ifacol.2018.09.048},
   url = {}
}
Download
Mejari, M., Naik, V.V., Piga, D., Bemporad, A. (2018). Energy disaggregation using piecewise affine regression and binary quadratic programming. In 2018 IEEE Conference on Decision and Control (cdc), pp. 3116–3121.

Energy disaggregation using piecewise affine regression and binary quadratic programming

@INPROCEEDINGS{piga2018h,
   title = {Energy disaggregation using piecewise affine regression and binary quadratic programming},
   booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},
   author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},
   pages = {3116--3121},
   year = {2018},
   doi = {10.1109/CDC.2018.8619175},
   url = {}
}
Download
Mejari, M., Piga, D., Bemporad, A. (2018). A bias-correction method for closed-loop identification of linear parameter-varying systems. Automatica 87, pp. 128–141.

A bias-correction method for closed-loop identification of linear parameter-varying systems

@ARTICLE{piga2018c,
   title = {A bias-correction method for closed-loop identification of linear parameter-varying systems},
   journal = {Automatica},
   volume = {87},
   author = {Mejari, M. and Piga, D. and Bemporad, A.},
   pages = {128--141},
   year = {2018},
   doi = {10.1016/j.automatica.2017.09.014},
   url = {}
}
Download
Piga, D., Formentin, S., Bemporad, A. (2018). Direct data-driven control of constrained systems. IEEE Transactions on Control Systems Technology 26(4), pp. 1422–1429.

Direct data-driven control of constrained systems

@ARTICLE{piga2018a,
   title = {Direct data-driven control of constrained systems},
   journal = {{IEEE} Transactions on Control Systems Technology},
   volume = {26},
   author = {Piga, D. and Formentin, S. and Bemporad, A.},
   number = {4},
   pages = {1422--1429},
   year = {2018},
   doi = {10.1109/TCST.2017.2702118},
   url = {}
}
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Scanagatta, M., Corani, G., de Campos, C.P., Zaffalon, M. (2018). Approximate structure learning for large Bayesian networks. Machine Learning 107(8-10), pp. 1209–1227.

Approximate structure learning for large Bayesian networks

@ARTICLE{scanagatta2018b,
   title = {Approximate structure learning for large {B}ayesian networks},
   journal = {Machine Learning},
   publisher = {Springer},
   volume = {107},
   author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},
   number = {8-10},
   pages = {1209--1227},
   year = {2018},
   doi = {10.1007/s10994-018-5701-9},
   url = {}
}
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Scanagatta, M., Corani, G., Zaffalon, M., Yoo, J., Kang, U. (2018). Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. International Journal of Approximate Reasoning 95, pp. 152–166.

Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets

@ARTICLE{scanagatta2018a,
   title = {Efficient learning of bounded-treewidth {B}ayesian networks from complete and incomplete data sets},
   journal = {International Journal of Approximate Reasoning},
   volume = {95},
   author = {Scanagatta, M. and Corani, G. and Zaffalon, M. and Yoo, J. and Kang, U.},
   pages = {152--166},
   year = {2018},
   doi = {10.1016/j.ijar.2018.02.004},
   url = {}
}
Download
Selvi, D., Piga, D., Bemporad, A. (2018). Towards direct data-driven model-free design of optimal controllers. In 2018 European Control Conference (ecc), pp. 2836–2841.

Towards direct data-driven model-free design of optimal controllers

@INPROCEEDINGS{piga2018g,
   title = {Towards direct data-driven model-free design of optimal controllers},
   booktitle = {2018 European Control Conference ({e}cc)},
   author = {Selvi, D. and Piga, D. and Bemporad, A.},
   pages = {2836--2841},
   year = {2018},
   doi = {10.23919/ECC.2018.8550184},
   url = {}
}
Download
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2017

Arnone, E., Azzimonti, L., Nobile, F., Sangalli, L.M. (2017). A time-dependent PDE regularization to model functional data defined over spatio-temporal domains. In Aneiros G., Bongiorno E.G., Cao R., Vieu P. (Ed), Functional Statistics and Related Fields, Springer International Publishing, pp. 41–44.

A time-dependent PDE regularization to model functional data defined over spatio-temporal domains

@INBOOK{azzimonti2017b,
   title = {A time-dependent {PDE} regularization to model functional data defined over spatio-temporal domains},
   editor = {Aneiros G., Bongiorno E.G., Cao R., Vieu P. },
   publisher = {Springer International Publishing},
   booktitle = {Functional Statistics and Related Fields},
   author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},
   pages = {41--44},
   year = {2017},
   doi = {10.1007/978-3-319-55846-2_6},
   url = {}
}
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Azzimonti, L., Corani, G., Zaffalon, M. (2017). Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables. In Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X. (Ed), 2017 IEEE 17th International Conference on Data Mining (ICDM), pp. 739–744.

Hierarchical Multinomial-Dirichlet model for the estimation of conditional probability tables

@INPROCEEDINGS{azzimonti2017c,
   title = {Hierarchical {M}ultinomial-{D}irichlet model for the estimation of conditional probability tables},
   editor = {Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X.},
   booktitle = {2017 {IEEE} 17th International Conference on Data Mining ({ICDM})},
   author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},
   pages = {739--744},
   year = {2017},
   doi = {10.1109/ICDM.2017.85},
   url = {}
}
Download
Balleri, A., Farina, A., Benavoli, A. (2017). Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems. IET Radar, Sonar & Navigation 11(7), pp. 1132–139.

Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems

@ARTICLE{benavoli2017a,
   title = {Coordination of optimal guidance law and adaptive radiated waveform for interception and rendezvous problems},
   journal = {{IET} Radar, Sonar & Navigation},
   publisher = {Institution of Engineering and Technology},
   volume = {11},
   author = {Balleri, A. and Farina, A. and Benavoli, A.},
   number = {7},
   pages = {1132--139},
   year = {2017},
   doi = {10.1049/iet-rsn.2016.0547},
   url = {}
}
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Benavoli, A., Corani, G., Demsar, J., Zaffalon, M. (2017). Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. Journal of Machine Learning Research 18(77), pp. 1–36.

Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis

@ARTICLE{benavoli2016e,
   title = {Time for a change: a tutorial for comparing multiple classifiers through {B}ayesian analysis},
   journal = {Journal of Machine Learning Research},
   volume = {18},
   author = {Benavoli, A. and Corani, G. and Demsar, J. and Zaffalon, M.},
   number = {77},
   pages = {1--36},
   year = {2017},
   doi = {},
   url = {http://jmlr.org/papers/v18/16-305.html}
}
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Benavoli, A., Facchini, A., Vicente-Perez, J., Zaffalon, M. (2017). A polarity theory for sets of desirable gambles. In Proc. ISIPTA '17 Int. Symposium on Imprecise Probability: Theories and Applications 62, PMLR, pp. 1–12.

A polarity theory for sets of desirable gambles

@INPROCEEDINGS{Benavoli2017c,
   title = {A polarity theory for sets of desirable gambles},
   publisher = {PMLR},
   volume = {62},
   booktitle = {Proc. {ISIPTA} '17 Int. Symposium on Imprecise Probability: Theories and Applications},
   author = {Benavoli, A. and Facchini, A. and Vicente-Perez, J. and Zaffalon, M.},
   pages = {1--12},
   year = {2017},
   doi = {},
   url = {http://proceedings.mlr.press/v62/benavoli17b/benavoli17b.pdf}
}
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Benavoli, A., Facchini, A., Piga, D., Zaffalon, M. (2017). SOS for bounded rationality. In Proceedings of Machine Learning Research 62, PMLR, pp. 25–36.

SOS for bounded rationality

@INPROCEEDINGS{Benavoli2017b,
   title = {{SOS} for bounded rationality},
   publisher = {PMLR},
   volume = {62},
   booktitle = {Proceedings of Machine Learning Research},
   author = {Benavoli, A. and Facchini, A. and Piga, D. and Zaffalon, M.},
   pages = {25--36},
   year = {2017},
   doi = {},
   url = {http://proceedings.mlr.press/v62/benavoli17a/benavoli17a.pdf}
}
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Benavoli, A., Facchini, A., Zaffalon, M. (2017). Bayes + Hilbert = Quantum Mechanics. In Proceedings of the 14th Interational Conference on Quantum Physics and Logic (qpl 2017), Nijmegen, the Netherlands, 3-7 July.

Bayes + Hilbert = Quantum Mechanics

@INPROCEEDINGS{Benavoli2017m,
   title = {Bayes + {H}ilbert = {Q}uantum {M}echanics},
   booktitle = {Proceedings of the 14th Interational Conference on Quantum Physics and Logic ({q}pl 2017), Nijmegen, the Netherlands, 3-7 July},
   author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},
   year = {2017},
   doi = {},
   url = {http://qpl.science.ru.nl/papers/QPL_2017_paper_4.pdf}
}
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Bucher, D., Mangili, F., Bonesana, C., Jonietz, D., Cellina, F., Raubal, M. (2017). Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior. In 33(1), pp. 1–2.

Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior

@INPROCEEDINGS{mangili2017c,
   title = {Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior},
   journal = {Computer Science - Research and Development},
   volume = {33},
   author = {Bucher, D. and Mangili, F. and Bonesana, C. and Jonietz, D. and Cellina, F. and Raubal, M.},
   number = {1},
   pages = {1--2},
   year = {2017},
   doi = {10.1007/s00450-017-0375-2},
   url = {}
}
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Corani, G., Benavoli, A., Demšar, J., Mangili, F., Zaffalon, M. (2017). Statistical comparison of classifiers through Bayesian hierarchical modelling. Machine Learning 106(11), pp. 1817–1837.

Statistical comparison of classifiers through Bayesian hierarchical modelling

@ARTICLE{corani2017a,
   title = {Statistical comparison of classifiers through {B}ayesian hierarchical modelling},
   journal = {Machine Learning},
   volume = {106},
   author = {Corani, G. and Benavoli, A. and Demšar, J. and Mangili, F. and Zaffalon, M.},
   number = {11},
   pages = {1817--1837},
   year = {2017},
   doi = {10.1007/s10994-017-5641-9},
   url = {}
}
Download
Cruder, C., Falla, D., Mangili, F., Azzimonti, L., Araújo, L., Williamon, A., Barbero, M. (2017). Profiling the location and extent of musicians' pain using digital pain drawings. PAIN Practice 18(1), pp. 53–66.

Profiling the location and extent of musicians' pain using digital pain drawings

@ARTICLE{mangili2017a,
   title = {Profiling the location and extent of musicians' pain using digital pain drawings},
   journal = {{PAIN} Practice},
   publisher = {Wiley},
   volume = {18},
   author = {Cruder, C. and Falla, D. and Mangili, F. and Azzimonti, L. and Ara\'ujo, L. and Williamon, A. and Barbero, M.},
   number = {1},
   pages = {53--66},
   year = {2017},
   doi = {10.1111/papr.12581},
   url = {}
}
Download
Gorini, F., Azzimonti, L., Delfanti, G., Scarfò, L., Scielzo, C., Bertilaccio, M.T., Ranghetti, P., Gulino, A., Doglioni, C., Napoli, A.D., Capri, M., Franceschi, C., Calligaris-Cappio, F., Ghia, P., Bellone, M., Dellabona, P., Casorati, G., de Lalla, C. (2017). Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis. Blood 129(26), pp. 3440–3451.

Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis

@ARTICLE{azzimonti2017a,
   title = {Invariant {NKT} cells contribute to {C}hronic {L}ymphocytic {L}eukemia surveillance and prognosis},
   journal = {Blood},
   volume = {129},
&nbs