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.

top## 2023

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

## Multi-agent active learning for distributed black-box optimization

## Nonlinear desirability as a linear classification problem

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

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

## Nonlinear desirability theory

## Correlated product of experts for sparse Gaussian process regression

(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.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2023). Multi-agent active learning for distributed black-box optimization. *IEEE Control Systems Letters* **7**, pp. 1488 - 1493.

@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 = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10107979}

}

Downloadtitle = {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 = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10107979}

}

(2023). Nonlinear desirability as a linear classification problem. *International Journal of Approximate Reasoning* **152**, pp. 1–32.

@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 = {}

}

Downloadtitle = {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 = {}

}

(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.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2023). Computation of parameter dependent robust invariant sets for lpv models with guaranteed performance. *Automatica* **151**, 110920.

@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 = {https://doi.org/10.1016/j.automatica.2023.110920},

url = {https://www.sciencedirect.com/science/article/pii/S0005109823000705}

}

Downloadtitle = {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 = {https://doi.org/10.1016/j.automatica.2023.110920},

url = {https://www.sciencedirect.com/science/article/pii/S0005109823000705}

}

(2023). Nonlinear desirability theory. *International Journal of Approximate Reasoning* **154**, pp. 176–199.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2023). Correlated product of experts for sparse Gaussian process regression. *Machine Learning*.

@ARTICLE{schurch2023,

title = {Correlated product of experts for sparse {G}aussian process regression},

journal = {Machine Learning},

author = {Sch\"urch, M. and Azzimonti, D. and Benavoli, A. and Zaffalon, M.},

year = {2023},

doi = {10.1007/s10994-022-06297-3},

url = {}

}

Downloadtitle = {Correlated product of experts for sparse {G}aussian process regression},

journal = {Machine Learning},

author = {Sch\"urch, M. and Azzimonti, D. and Benavoli, A. and Zaffalon, M.},

year = {2023},

doi = {10.1007/s10994-022-06297-3},

url = {}

}

top## 2022

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

## Intelligent tutoring systems by Bayesian nets with noisy gates

## A bayesian hierarchical score for structure learning from related data sets

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

## Information algebras in the theory of imprecise probabilities

## Information algebras in the theory of imprecise probabilities, an extension

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

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

## Virtual operators with self and transfer learning ability in EDM

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

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

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

## 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

## A survey on computational taste predictors

## Multipartite entanglement in qudit hypergraph states

## Modelling assessment rubrics through Bayesian networks: a pragmatic approach

## Informed classification of sweeteners/bitterants compounds via explainable machine learning

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

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

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

## Visual servoing with geometrically interpretable neural perception

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

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

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

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

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

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

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

## Robot joint friction compensation learning enhanced by 6D virtual sensor

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

## User-centered back-support exoskeleton: design and prototyping

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

## Comments on: Hybrid Semiparametric Bayesian Networks

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

## Achieving fairness with a simple ridge penalty

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

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

## On the asymptotic behavior of Jacobi polynomials with first varying parameter

## Bounding counterfactuals under selection bias

(2022). Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach. *Scientific Reports, Nature Publishing Group* **12**(1), 21735.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Intelligent tutoring systems by Bayesian nets with noisy gates. In **35**.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). A bayesian hierarchical score for structure learning from related data sets. *International Journal of Approximate Reasoning* **142**, pp. 248–265.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Active preference-based optimization for human-in-the-loop feature selection. *European Journal of Control* **66**, 100647.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Information algebras in the theory of imprecise probabilities. *International Journal of Approximate Reasoning* **142**, pp. 383–416.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Information algebras in the theory of imprecise probabilities, an extension. *International Journal of Approximate Reasoning* **150**, pp. 311–336.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). TCR-engineered iNKT cells induce robust antitumor response by dual targeting cancer and suppressive myeloid cells. *Science Immunology* **7**(74), eabn6563.

@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 = {}

}

Downloadtitle = {{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 = {}

}

(2022). Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. *Computers in Biology and Medicine* **147**, 105740.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Virtual operators with self and transfer learning ability in EDM. *Procedia CIRP* **113**, pp. 17–22.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Improved Impedance/Admittance switching controller for the interaction with a variable stiffness environment. *Complex Engineering Systems* **2**(3), 12.

@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 = {}

}

Downloadtitle = {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 = {}

}

(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)*.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Data-driven statistical analysis for discharge position prediction on Wire EDM. *Procedia CIRP* **113**, pp. 143–148.

@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 = {}

}

Downloadtitle = {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 = {}

}

(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.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). A survey on computational taste predictors. *European Food Research and Technology* **248**(9), pp. 2215–2235.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Multipartite entanglement in qudit hypergraph states. *Journal of Physics A: Mathematical and Theoretical* **55**(41), 415301.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2022). Modelling assessment rubrics through Bayesian networks: a pragmatic approach. *Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)*.

@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.},

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}

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author = {Mangili, F. and Adorni, G. and Piatti, A. and Bonesana, C. and Antonucci, A.},

year = {2022},

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}

(2022). Informed classification of sweeteners/bitterants compounds via explainable machine learning. *Current Research in Food Science* **5**, pp. 2270–2280.

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title = {Informed classification of sweeteners/bitterants compounds via explainable machine learning},

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year = {2022},

doi = {10.1016/j.crfs.2022.11.014},

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}

(2022). Direct identification of continuous-time lpv state-space models via an integral architecture. *Automatica* **142**, 110407.

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title = {Direct identification of continuous-time lpv state-space models via an integral architecture},

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(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).

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title = {Maximum—a posteriori estimation of linear time-invariant state-space models via efficient monte-carlo sampling},

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number = {1},

year = {2022},

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(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)*.

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(2022). Visual servoing with geometrically interpretable neural perception. In *2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, pp. 5300–5306.

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title = {Visual servoing with geometrically interpretable neural perception},

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(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.

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(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.

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(2022). Grasping learning, optimization, and knowledge transfer in the robotics field. *Scientific Reports* **12**(1), 4481.

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year = {2022},

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(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.

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(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.

@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.},

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number = {6},

pages = {3220},

year = {2022},

doi = {10.3390/ijerph19063220},

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}

(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.

@ARTICLE{mangili2022c,

title = {The effects of microclimatic winter conditions in urban areas on the risk of establishment for {A}edes albopictus},

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(2022). Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury. *JCI Insight* **7**(18).

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title = {Impaired fatty acid metabolism perpetuates lipotoxicity along the transition to chronic kidney injury},

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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.},

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number = {18},

year = {2022},

doi = {10.1172/jci.insight.161783},

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}

(2022). Robot joint friction compensation learning enhanced by 6D virtual sensor. *International Journal of Robust and Nonlinear Control* **32**(9), pp. 5741–5763.

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title = {Robot joint friction compensation learning enhanced by {6D} virtual sensor},

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(2022). Robot end-effector mounted camera pose optimization in object detection-based tasks. *Journal of Intelligent & Robotic Systems* **104**(1), 16.

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(2022). User-centered back-support exoskeleton: design and prototyping. *Procedia CIRP* **107**, pp. 522–527.

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title = {User-centered back-support exoskeleton: design and prototyping},

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(2022). Q-learning-based model predictive variable impedance control for physical human-robot collaboration. *Artificial Intelligence*, 103771.

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(2022). Comments on: Hybrid Semiparametric Bayesian Networks. *TEST* **31**, pp. 328–330.

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title = {Comments on: {H}ybrid {S}emiparametric {B}ayesian {N}etworks},

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}

(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.

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(2022). Achieving fairness with a simple ridge penalty. *Statistics and Computing* **32**(5), 77.

@ARTICLE{scutari22a,

title = {Achieving fairness with a simple ridge penalty},

journal = {Statistics and Computing},

volume = {32},

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(2022). Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning. *Autonomous Robots* **46**(3), pp. 483–498.

@ARTICLE{Roveda2022b,

title = {Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning},

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}

(2022). Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-MRI case. *IEEE Transactions on Computational Imaging* **8**, pp. 133–147.

@ARTICLE{cannelli2022a,

title = {Kernel regression imputation in manifolds via bi-linear modeling: the dynamic-{MRI} case},

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volume = {8},

author = {Slavakis, K. and Shetty, G.N. and Cannelli, L. and Scutari, G. and Nakarmi, U. and Ying, L.},

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}

(2022). On the asymptotic behavior of Jacobi polynomials with first varying parameter. *Journal of Approximation Theory* (277), 105702.

@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.},

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year = {2022},

doi = {10.1016/j.jat.2022.105702},

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}

(2022). Bounding counterfactuals under selection bias. In Salmerón, A., Rumí, R. (Eds), *Proceedings of PGM 2022*, PMLR **186**, JMLR.org, pp. 289–300.

@INPROCEEDINGS{zaffalon2022a,

title = {Bounding counterfactuals under selection bias},

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volume = {186},

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top## 2021

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

## A new score for adaptive tests in Bayesian and credal networks

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

## Global optimization based on active preference learning with radial basis functions

## Preferential bayesian optimisation with skew gaussian processes

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

## State Space approximation of Gaussian Processes for time-series forecasting

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

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

## Artificial intelligence in thyroid field. A comprehensive review

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

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

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

## CREPO: an open repository to benchmark credal network algorithms

## Nonlinear desirability as a linear classification problem

## Algebras of sets and coherent sets of gambles

## Joint desirability foundations of social choice and opinion pooling

## Time series forecasting with Gaussian Processes needs priors

## dynoNet: a neural network architecture for learning dynamical systems

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

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

## Neural machine translation for conditional generation of novel procedures

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

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

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

## Sparse information filter for fast gaussian process regression

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

## Information algebras of coherent sets of gambles in general possibility spaces

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

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

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

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

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

## Probabilistic models with deep neural networks

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

## Relation clustering in narrative knowledge graphs

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

## Angrybert: joint learning target and emotion for hate speech detection

## Deep learning with transfer functions: New applications in system identification

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

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

## Enhancing object detection performance through sensor pose Deﬁnition with bayesian optimization

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

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

## Sensorless optimal switching Impact/Force controller

## Sensorless optimal interaction control exploiting environment stiffness estimation

## Optimal direct data-driven control with stability guarantees

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

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

## Explicit counterexamples to Schäffer's conjecture

## Logic and model checking by imprecise probabilistic interpreted systems

## Robust model checking with imprecise markov reward models

## Causal expectation-maximisation

## Desirability foundations of robust rational decision making

## The sure thing

## Preference-based MPC calibration

(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)*.

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(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.

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(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.

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(2021). Global optimization based on active preference learning with radial basis functions. *Machine Learning* **110**, pp. 417–448.

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(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.

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(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.

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(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.

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(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.

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(2021). Model structure selection for switched narx system identification: a randomized approach. *Automatica* **125**, 109415.

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(2021). Artificial intelligence in thyroid field. A comprehensive review. *Cancers* **13**(19), 4740.

@ARTICLE{azzimonti2021a,

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(2021). Learning bayesian networks from incomplete data with the node-averaged likelihood. *International Journal of Approximate Reasoning* **138**, pp. 145–160.

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(2021). ADAPQUEST: a software for web-based adaptive questionnaires based on Bayesian networks. In *AI4EDU: Artificial Intelligence for Education (@ Ijcai2021)*, Virtual Event.

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(2021). A constraint-based algorithm for the structural learning of continuous-time bayesian networks. *International Journal of Approximate Reasoning* **138**, pp. 105–122.

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(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.

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(2021). Nonlinear desirability as a linear classification problem. In *Proceedings of Machine Learning Research* **147**, pp. 617–71.

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(2021). Algebras of sets and coherent sets of gambles. In *Symbolic and Quantitative Approaches to Reasoning with Uncertainty*, Springer International Publishing, pp. 603–615.

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(2021). Joint desirability foundations of social choice and opinion pooling. *Annals of Mathematics and Artificial Intelligence* **89**(10–11), pp. 965–1011.

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(2021). Time series forecasting with Gaussian Processes needs priors. In *European Conference on Machine Learning and Knowledge Discovery in Databases*, pp. 103–117.

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(2021). dynoNet: a neural network architecture for learning dynamical systems. *International Journal of Adaptive Control and Signal Processing* **35**, pp. 612–626.

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(2021). Continuous-time system identification with neural networks: model structures and fitting criteria. *European Journal of Control* **59**, pp. 69–81.

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(2021). Review on patient-cooperative control strategies for upper-limb rehabilitation exoskeletons. *Frontiers in Robotics and AI* **8**.

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(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.

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(2021). Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models. *International Journal of Intelligent Systems* **36**(11), pp. 6913–6943.

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year = {2021},

doi = {10.1109/TCST.2021.3061091},

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(2021). Optimal direct data-driven control with stability guarantees. *European Journal of Control* **59**, pp. 175–187.

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(2021). Decentralized multi-agent control of a manipulator in continuous task learning. *MDPI Applied Science* **11**(21), 10227.

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(2021). Deep self-optimizing artificial intelligence for tactical analysis, training and optimization. In , NATO.

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(2021). Explicit counterexamples to Schäffer's conjecture. *Journal de Mathématiques Pures et Appliquées* **146**, pp. 1–30.

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(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.

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(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.

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(2021). Causal expectation-maximisation. In *WHY-21 @ NeurIPS 2021*.

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(2021). Desirability foundations of robust rational decision making. *Synthese* **198**(27), pp. S6529–S6570.

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(2021). The sure thing. In De Bock, J., Cano, A., Miranda, E., Moral, S. (Ed), *ISIPTA 2021*, PMLR **147**, JMLR.org, pp. 342–351.

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(2021). Preference-based MPC calibration. In *2021 European Control Conference (ECC)*, Napoli, Italy, pp. 638–645.

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top## 2020

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

## Approximate MMAP by marginal search

## Structure learning from related data sets with a hierarchical Bayesian score

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

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

## Skew gaussian processes for classification

## Identifiability and consistency of bayesian network structure learning from incomplete data

## Constraint-based learning for continuous-time bayesian networks

## Shrinkage strategies for structure selection and identification of piecewise affine models

## A machine learning approach to relationships among alexithymia components

## Credici: a java library for causal inference by credal networks

## Probabilistic graphical models with neural networks in inferpy

## Asynchronous optimization over graphs: linear convergence under error bound conditions

## Social pooling of beliefs and values with desirability

## Detecting correlation between extreme probability events

## Probabilistic reconciliation of hierarchical forecast via Bayes’ rule

## Inferpy: probabilistic modeling with deep neural networks made easy

## An interdisciplinary examination of stress and injury occurrence in athletes

## Model structures and fitting criteria for system identification with neural networks

## Efficient Calibration of Embedded MPC

## Poset representations for sets of elementary triplets

## Building causal interaction models by recursive unfolding

## Learning probabilistic sentential decision diagrams by sampling

## CREMA: a Java library for credal network inference

## Temporal embeddings and transformer models for narrative text understanding

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

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

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

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

## Conversational recommender system by Bayesian methods

## A Bayesian approach to conversational recommendation systems

## Tractable inference in credal sentential decision diagrams

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

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

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

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

## Compatibility, desirability, and the running intersection property

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

## Estimation of jump box–jenkins models

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

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

## Robot control parameters auto-tuning in trajectory tracking applications

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

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

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

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

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

## Interaction force computation exploiting environment stiﬀness estimation for sensorless robot applications

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

## Hard and soft em in bayesian network learning from incomplete data

## Recursive estimation for sparse gaussian process regression

## Learning continuous control actions for robotic grasping with reinforcement learning

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

## Tactile sensing with gesture-controlled collaborative robot

## Interpolation without commutants

## Temporal word embeddings for narrative understanding

## Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference

## Structural causal models are (solvable by) credal networks

(2020). A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence. *IEEE Computer* **53**, pp. 18–28.

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(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.

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(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.

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(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.

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(2020). Reducing probes for quality of transmission estimation in optical networks with active learning. *J. Opt. Commun. Netw.* **12**(1), pp. A38–A48.

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(2020). Skew gaussian processes for classification. *Machine Learning* **109**(9), pp. 1877–1902.

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(2020). Identifiability and consistency of bayesian network structure learning from incomplete data. *Proceedings of Machine Learning Research (PGM 2020)* **138**, pp. 29–40.

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(2020). Constraint-based learning for continuous-time bayesian networks. *Proceedings of Machine Learning Research (PGM 2020)* **138**, pp. 41–52.

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(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.

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(2020). A machine learning approach to relationships among alexithymia components. *Psychiatria Danubina* **32**(Suppl. 1), pp. 180–187.

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(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.

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(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.

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year = {2020},

doi = {https://doi.org/10.1002/rnc.5198},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.5198}

}

(2020). Compatibility, desirability, and the running intersection property. *Artificial Intelligence* **283**, 103724.

@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},

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year = {2020},

doi = {10.1016/j.artint.2020.103274},

url = {}

}

(2020). Rao-Blackwellized sampling for batch and recursive Bayesian inference of piecewise affine models. *Automatica* **117**, 109002.

@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},

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journal = {Automatica},

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pages = {109002},

year = {2020},

doi = {10.1016/j.automatica.2020.109002},

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}

(2020). Estimation of jump box–jenkins models. *Automatica* **120**, 109126.

@ARTICLE{piga2020c,

title = {Estimation of jump box--jenkins models},

journal = {Automatica},

volume = {120},

author = {Piga, D. and Breschi, V. and Bemporad, A.},

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year = {2020},

doi = {10.1016/j.automatica.2020.109126},

url = {http://www.sciencedirect.com/science/article/pii/S0005109820303241}

}

(2020). 6D virtual sensor for wrench estimation in robotized interaction tasks exploiting extended Kalman filter. *MDPI Machines* **8**(4), 67.

@ARTICLE{Roveda2020b,

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number = {4},

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year = {2020},

doi = {10.3390/machines8040067},

url = {}

}

(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.

@INPROCEEDINGS{Roveda2020f,

title = {A control framework definition to overcome position/interaction dynamics uncertainties in force-controlled tasks},

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year = {2020},

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(2020). Robot control parameters auto-tuning in trajectory tracking applications. *Control Engineering Practice* **101**, 104488.

@ARTICLE{roveda2020a,

title = {Robot control parameters auto-tuning in trajectory tracking applications},

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(2020). One-stage auto-tuning procedure of robot dynamics and control parameters for trajectory tracking applications. In *Ubiquitous Robots 2020*, pp. 105–112.

@INPROCEEDINGS{Roveda2020h,

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(2020). Human–robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian optimization. *Robotics and Autonomous Systems* **136**, 103711.

@ARTICLE{Roveda2020j,

title = {Human--robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via {B}ayesian optimization},

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year = {2020},

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(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.

@INPROCEEDINGS{Roveda2020k,

title = {Assembly task learning and optimization through {H}uman’s demonstration and machine learning},

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(2020). Model-based reinforcement learning variable impedance control for human-robot collaboration. *Journal of Intelligent & Robotic Systems* **100**(2), pp. 417–433.

@ARTICLE{Roveda2020c,

title = {Model-based reinforcement learning variable impedance control for human-robot collaboration},

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(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.

@ARTICLE{Roveda2020d,

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(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.

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(2020). Design methodology of an active back-support exoskeleton with adaptable backbone-based kinematics. *International Journal of Industrial Ergonomics* **79**, 102991.

@ARTICLE{Roveda2020e,

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(2020). Hard and soft em in bayesian network learning from incomplete data. *Algorithms* **13**(12), 329.

@ARTICLE{scutari20h,

title = {Hard and soft em in bayesian network learning from incomplete data},

journal = {Algorithms},

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number = {12},

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(2020). Recursive estimation for sparse gaussian process regression. *Automatica* **120**, 109127.

@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.},

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year = {2020},

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}

(2020). Learning continuous control actions for robotic grasping with reinforcement learning. In *IEEE International Conference on Systems, Man, and Cybernetics*, pp. 4066–4072.

@INPROCEEDINGS{Roveda2020l,

title = {Learning continuous control actions for robotic grasping with reinforcement learning},

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Downloadtitle = {Learning continuous control actions for robotic grasping with reinforcement learning},

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pages = {4066--4072},

year = {2020},

doi = {10.1109/SMC42975.2020.9282951},

url = {}

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(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.

@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.},

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number = {127},

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year = {2020},

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(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.

@INPROCEEDINGS{Roveda2020i,

title = {Tactile sensing with gesture-controlled collaborative robot},

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pages = {364--368},

year = {2020},

doi = {10.1109/MetroInd4.0IoT48571.2020.9138183},

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}

(2020). Interpolation without commutants. *Journal of Operator Theory* **84**(1), pp. 239–256.

@ARTICLE{szehr2020aa,

title = {Interpolation without commutants},

journal = {Journal of Operator Theory},

volume = {84},

author = {Szehr, O. and Zarouf, R.},

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doi = {10.7900/jot.2019may21.2264},

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}

(2020). Temporal word embeddings for narrative understanding. In *12th International Conference on Machine Learning and Computing (ICMLC 2020)* (5), ACM, pp. 68–72.

@INPROCEEDINGS{supsi2020a,

title = {Temporal word embeddings for narrative understanding},

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(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.

@INPROCEEDINGS{corani2019e,

title = {Sampling subgraphs with guaranteed treewidth for accurate and efficient graphical inference},

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(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.

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top## 2019

## Reliable discretisation of deterministic equations in Bayesian networks

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

@INPROCEEDINGS{supsi2019c,

title = {Reliable discretisation of deterministic equations in {B}ayesian networks},

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Downloadtitle = {Reliable discretisation of deterministic equations in {B}ayesian networks},

publisher = {AAAI Press},

booktitle = {Proceedings of the 32nd International Flairs Conference ({FLAIRS}-32)},

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doi = {},

url = {}

}

(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.

@INPROCEEDINGS{supsi2019b,

title = {Credal sentential decision diagrams},

publisher = {PMLR},

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booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},

author = {Antonucci, A. and Facchini, A. and Mattei, L.},

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booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications ({ISIPTA} '19)},

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pages = {14--22},

year = {2019},

doi = {},

url = {https://proceedings.mlr.press/v103/antonucci19a.html}

}

(2019). Modeling spatially dependent functional data via regression with differential regularization. *Journal of Multivariate Analysis* **170**, pp. 275–295.

@ARTICLE{azzimonti2018a,

title = {Modeling spatially dependent functional data via regression with differential regularization},

journal = {Journal of Multivariate Analysis},

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year = {2019},

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(2019). Hierarchical estimation of parameters in Bayesian networks. *Computational Statistics and Data Analysis* **137**, pp. 67–91.

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(2019). Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. *Technometrics* **61**(4), pp. 474–493.

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(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.

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(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.

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(2019). Online end-use energy disaggregation via jump linear models. *Control Engineering Practice* **89**, pp. 30–42.

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(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.

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(2019). Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics. *Engineering Fracture Mechanics* **205**, pp. 439–454.

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(2019). A large scale, app-based behaviour change experiment persuading sustainable mobility patterns: methods, results and lessons learnt. *Sustainability* **11**(9), 2674.

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(2019). Improving spaCy dependency annotation and PoS tagging web service using independent NER services. *Genomics Inform* **17**(2), e21.

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(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.

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(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.

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(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.

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(2019). Oger++: hybrid multi-type entity recognition. *Journal of Cheminformatics* **11**(1), 7.

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(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.

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(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.

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(2019). Introduction to BLAH5 special issue: recent progress on interoperability of biomedical text mining. *Genomics Inform* **17**(2), e12.

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(2019). Exploring the space of probabilistic sentential decision diagrams. In *Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning*.

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(2019). Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks. *MDPI Robotics*.

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(2019). Realization and identification algorithm for stochastic lpv state-space models with exogenous inputs.. *IFAC-PapersOnLine* **52**(28), pp. 13–19.

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(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.

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(2019). Kernelized identification of linear parameter-varying models with linear fractional representation. In *2019 European Control Conference (ecc)*, Naples, Italy.

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(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.

@INCOLLECTION{zaffalon2018a,

title = {Compatibility, coherence and the {RIP}},

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series = {Advances in Intelligent Systems and Computing},

volume = {832},

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Downloadtitle = {Compatibility, coherence and the {RIP}},

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}

(2019). A tutorial on machine learning for failure management in optical networks. *Journal of Lightwave Technology* **37**(16), pp. 4125–4139.

@ARTICLE{corani2019b,

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(2019). Reverse engineering creativity into interpretable neural networks. In *Future of Information and Communications*, Lecture Notes in Networks and Systems **70**, pp. 235–247.

@INPROCEEDINGS{oita2019innGenuity,

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(2019). Incremental alignment of metaphoric language model for poetry composition. In *Intelligent Computing*, Springer, "Advances in Intelligent Systems and Computing", pp. 834–845.

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(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.

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(2019). Semialgebraic outer approximations for set-valued nonlinear filtering. In *2019 European Control Conference (ECC)*, Naples, Italy.

@INPROCEEDINGS{piga2019f,

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}

(2019). Performance-oriented model learning for data-driven MPC design. *IEEE Control Systems Letters* **3**(3), pp. 577–582.

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(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.

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(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.

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(2019). Revisiting the decay of scientific email addresses. *bioRxiv*.

@ARTICLE{rinaldi2019j,

title = {Revisiting the decay of scientific email addresses},

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(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.

@ARTICLE{Roveda2019a,

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

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year = {2019},

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(2019). Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects. *Computers & Operations Research* **108**, pp. 112–120.

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title = {Hybrid heuristic for the optimal design of photovoltaic installations considering mismatch loss effects},

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pages = {112--120},

year = {2019},

doi = {10.1016/j.cor.2019.04.009},

url = {}

}

(2019). Efficient feature selection using shrinkage estimators. *Machine Learning* **108**(8), pp. 1261–1286.

@ARTICLE{azzimonti2019b,

title = {Efficient feature selection using shrinkage estimators},

journal = {Machine Learning},

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author = {Sechidis, K. and Azzimonti, L. and Pocock, A. and Corani, G. and Weatherall, J. and Brown, G.},

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year = {2019},

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url = {}

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(2019). Natural language processing of clinical notes on chronic diseases: systematic review. *JMIR Med Inform* **7**(2), e12239.

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title = {Natural language processing of clinical notes on chronic diseases: systematic review},

journal = {{JMIR} Med Inform},

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url = {http://medinform.jmir.org/2019/2/e12239/}

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(2019). A new approach and gold standard toward author disambiguation in MEDLINE. *J Am Med Inform Assoc* **26**(10), pp. 1037–1045.

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title = {A new approach and gold standard toward author disambiguation in {MEDLINE}},

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top## 2018

## A credal extension of independent choice logic

## Set-valued probabilistic sentential decision diagrams

## Fitting jump models

## Prediction error methods in learning jump ARMAX models

## Kalman filtering for energy disaggregation

## Jump model learning and filtering for energy end-use disaggregation

## Entropy-based pruning for learning Bayesian networks using BIC

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

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

## Reliable uncertain evidence modeling in Bayesian networks by credal networks

## Imaginary kinematics

## Regularized moving-horizon PWA regression for LPV system identification

## Energy disaggregation using piecewise affine regression and binary quadratic programming

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

## Direct data-driven control of constrained systems

## Approximate structure learning for large Bayesian networks

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

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

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

@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}

}

Downloadtitle = {A credal extension of independent choice logic},

booktitle = {Proceedings of the 12th International Conference on Scalable Uncertainty Management ({SUM} 2018)},

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pages = {35--49},

year = {2018},

doi = {10.1007/978-3-030-00461-3_3},

url = {https://arxiv.org/abs/1806.08298}

}

(2018). Set-valued probabilistic sentential decision diagrams. In *Proceedings of the 5th Workshop on Probabilistic Logic Programming*, pp. 3–8.

@INPROCEEDINGS{antonucci2018d,

title = {Set-valued probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 5th Workshop on Probabilistic Logic Programming},

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year = {2018},

doi = {},

url = {https://ceur-ws.org/Vol-2219/}

}

Downloadtitle = {Set-valued probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 5th Workshop on Probabilistic Logic Programming},

author = {Antonucci, A. and Facchini, A.},

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year = {2018},

doi = {},

url = {https://ceur-ws.org/Vol-2219/}

}

(2018). Fitting jump models. *Automatica* **96**, pp. 11–21.

@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},

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}

Downloadtitle = {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 = {}

}

(2018). Prediction error methods in learning jump ARMAX models. In *2018 IEEE Conference on Decision and Control (cdc)*, pp. 2247–2252.

@INPROCEEDINGS{piga2018i,

title = {Prediction error methods in learning jump {ARMAX} models},

booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},

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doi = {10.1109/CDC.2018.8619819},

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Downloadtitle = {Prediction error methods in learning jump {ARMAX} models},

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author = {Breschi, V. and Bemporad, A. and Piga, D. and Boyd, S.},

pages = {2247--2252},

year = {2018},

doi = {10.1109/CDC.2018.8619819},

url = {}

}

(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.

@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.},

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year = {2018},

doi = {10.1016/j.ifacol.2018.06.219},

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Downloadtitle = {Kalman filtering for energy disaggregation},

journal = {{IFAC}-{PapersOnLine}},

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number = {5},

pages = {108--113},

year = {2018},

doi = {10.1016/j.ifacol.2018.06.219},

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(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.

@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},

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}

Downloadtitle = {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 = {}

}

(2018). Entropy-based pruning for learning Bayesian networks using BIC. *Artificial Intelligence* **260**, pp. 42–50.

@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},

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Downloadtitle = {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 = {}

}

(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).

@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 = {}

}

Downloadtitle = {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 = {}

}

(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.

@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 = {}

}

Downloadtitle = {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 = {}

}

(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.

@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 = {}

}

Downloadtitle = {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 = {}

}

(2018). Imaginary kinematics. In *Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence*, AUAI Press, pp. 104–113.

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(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.

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title = {Regularized moving-horizon {PWA} regression for {LPV} system identification},

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(2018). Energy disaggregation using piecewise affine regression and binary quadratic programming. In *2018 IEEE Conference on Decision and Control (cdc)*, pp. 3116–3121.

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booktitle = {2018 {IEEE} Conference on Decision and Control ({c}dc)},

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(2018). A bias-correction method for closed-loop identification of linear parameter-varying systems. *Automatica* **87**, pp. 128–141.

@ARTICLE{piga2018c,

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(2018). Direct data-driven control of constrained systems. *IEEE Transactions on Control Systems Technology* **26**(4), pp. 1422–1429.

@ARTICLE{piga2018a,

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(2018). Approximate structure learning for large Bayesian networks. *Machine Learning* **107**(8-10), pp. 1209–1227.

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(2018). Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. *International Journal of Approximate Reasoning* **95**, pp. 152–166.

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(2018). Towards direct data-driven model-free design of optimal controllers. In *2018 European Control Conference (ecc)*, pp. 2836–2841.

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top## 2017

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

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

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

## Statistical comparison of classifiers through Bayesian hierarchical modelling

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

## Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis

## Reliable knowledge-based adaptive tests by credal networks

## A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices

## Improved local search in Bayesian networks structure learning

## Technical gestures recognition by set-valued hidden Markov models with prior knowledge

## Full conglomerability, continuity and marginal extension

## Full conglomerability

## Axiomatising incomplete preferences through sets of desirable gambles

(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.

@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. },

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booktitle = {Functional Statistics and Related Fields},

author = {Arnone, E. and Azzimonti, L. and Nobile, F. and Sangalli, L.M.},

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year = {2017},

doi = {10.1007/978-3-319-55846-2_6},

url = {}

}

(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.

@INPROCEEDINGS{azzimonti2017c,

title = {Hierarchical {M}ultinomial-{D}irichlet model for the estimation of conditional probability tables},

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booktitle = {2017 {IEEE} 17th International Conference on Data Mining ({ICDM})},

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year = {2017},

doi = {10.1109/ICDM.2017.85},

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}

(2017). Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior. In **33**(1), pp. 1–2.

@INPROCEEDINGS{mangili2017c,

title = {Demo abstract: extracting eco-feedback information from automatic activity tracking to promote energy-efficient individual mobility behavior},

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number = {1},

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year = {2017},

doi = {10.1007/s00450-017-0375-2},

url = {}

}

(2017). Statistical comparison of classifiers through Bayesian hierarchical modelling. *Machine Learning* **106**(11), pp. 1817–1837.

@ARTICLE{corani2017a,

title = {Statistical comparison of classifiers through {B}ayesian hierarchical modelling},

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author = {Corani, G. and Benavoli, A. and Demšar, J. and Mangili, F. and Zaffalon, M.},

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(2017). Profiling the location and extent of musicians' pain using digital pain drawings. *PAIN Practice* **18**(1), pp. 53–66.

@ARTICLE{mangili2017a,

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(2017). Invariant NKT cells contribute to Chronic Lymphocytic Leukemia surveillance and prognosis. *Blood* **129**(26), pp. 3440–3451.

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year = {2017},

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(2017). Reliable knowledge-based adaptive tests by credal networks. In Antonucci, A., Cholvy, L., Papini, O. (Eds), *Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2017*, Lecture Notes in Computer Science **10369**, Springer, Cham, pp. 282–291.

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(2017). A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices. *IEEE Transactions on Automatic Control* **PP**(99).

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(2017). Improved local search in Bayesian networks structure learning. In Antti Hyttinen, Joe Suzuki, Brandon Malone (Eds), *Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks (AMBN)*, Proceedings of Machine Learning Research **73**, PMLR, pp. 45–56.

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(2017). Technical gestures recognition by set-valued hidden Markov models with prior knowledge. In Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Soft Methods for Data Science*, Advances in Intelligent Systems and Computing **456**, Springer, pp. 455–462.

@INCOLLECTION{antonucci2016a,

title = {Technical gestures recognition by set-valued hidden {M}arkov models with prior knowledge},

editor = {Ferraro, M. B. and Giordani, P. and Vantaggi, B. and Gagolewski, M. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},

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pages = {455--462},

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doi = {10.1007/978-3-319-42972-4_56},

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}

(2017). Full conglomerability, continuity and marginal extension. In Ferraro, M. B., Giordani, P., Vantaggi, B., Gagolewski, M., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Soft Methods for Data Science*, Advances in Intelligent Systems and Computing **456**, Springer, pp. 355–362.

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(2017). Full conglomerability. *Journal of Statistical Theory and Practice* **11**(4), pp. 634–669.

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(2017). Axiomatising incomplete preferences through sets of desirable gambles. *Journal of Artificial Intelligence Research* **60**, pp. 1057–1126.

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top## 2016

## The multilabel naive credal classifier

## Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App

## Evaluating interval-valued influence diagrams

## Joint analysis of multiple algorithms and performance measures

## Learning extended tree augmented naive structures

## Air pollution prediction via multi-label classification

## Hierarchical Bayesian LASSO for a negative binomial regression

## Computational study of the fluid-dynamics in carotids before and after endarterectomy

## A prior near-ignorance Gaussian process model for nonparametric regression

## Adaptive testing by Bayesian networks with application to language assessment

## Hidden Markov models with set-valued parameters

## Conformity and independence with coherent lower previsions

## Bayesian network data imputation with application to survival tree analysis

## Learning treewidth-bounded Bayesian networks with thousands of variables

(2016). The multilabel naive credal classifier. *International Journal of Approximate Reasoning* **83**, pp. 320–336.

@ARTICLE{antonucci2016c,

title = {The multilabel naive credal classifier},

journal = {International Journal of Approximate Reasoning},

volume = {83},

author = {Antonucci, A. and Corani, G.},

pages = {320--336},

year = {2016},

doi = {10.1016/j.ijar.2016.10.006},

url = {}

}

Downloadtitle = {The multilabel naive credal classifier},

journal = {International Journal of Approximate Reasoning},

volume = {83},

author = {Antonucci, A. and Corani, G.},

pages = {320--336},

year = {2016},

doi = {10.1016/j.ijar.2016.10.006},

url = {}

}

(2016). Exploiting fitness apps for sustainable mobility - challenges deploying the GoEco! App. In *Proceedings of the 2016 conference ICT for Sustainability*, Advances in Computer Science Research, pp. 89–98.

@INPROCEEDINGS{mangili2016c,

title = {Exploiting fitness apps for sustainable mobility - challenges deploying the {GoEco}! App},

series = {Advances in Computer Science Research},

booktitle = {Proceedings of the 2016 {c}onference {ICT} for Sustainability},

author = {Bucher, D. and Cellina, F. and Mangili, F. and Raubal, M. and Rudel, R. and Rizzoli, A.E. and Elabed, O.},

pages = {89--98},

year = {2016},

doi = {doi:10.2991/ict4s-16.2016.11},

url = {}

}

Downloadtitle = {Exploiting fitness apps for sustainable mobility - challenges deploying the {GoEco}! App},

series = {Advances in Computer Science Research},

booktitle = {Proceedings of the 2016 {c}onference {ICT} for Sustainability},

author = {Bucher, D. and Cellina, F. and Mangili, F. and Raubal, M. and Rudel, R. and Rizzoli, A.E. and Elabed, O.},

pages = {89--98},

year = {2016},

doi = {doi:10.2991/ict4s-16.2016.11},

url = {}

}

(2016). Evaluating interval-valued influence diagrams. *International Journal of Approximate Reasoning* **80**, pp. 393–411.

@ARTICLE{antonucci2016b,

title = {Evaluating interval-valued influence diagrams},

journal = {International Journal of Approximate Reasoning},

volume = {80},

author = {Caba\~nas, R. and Antonucci, A. and Cano, A. and G\'omez-Olmedo, M.},

pages = {393--411},

year = {2016},

doi = {10.1016/j.ijar.2016.05.004},

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journal = {International Journal of Approximate Reasoning},

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pages = {393--411},

year = {2016},

doi = {10.1016/j.ijar.2016.05.004},

url = {}

}

(2016). Joint analysis of multiple algorithms and performance measures. *New Generation Computing*, pp. 1–18.

@ARTICLE{deCampos2016,

title = {Joint analysis of multiple algorithms and performance measures},

journal = {New Generation Computing},

author = {de Campos, C.P. and Benavoli, A.},

pages = {1--18},

year = {2016},

doi = {10.1007/s00354-016-0005-8},

url = {http://people.idsia.ch/~alessio/decampos-benavoli-ngc2016.pdf}

}

Downloadtitle = {Joint analysis of multiple algorithms and performance measures},

journal = {New Generation Computing},

author = {de Campos, C.P. and Benavoli, A.},

pages = {1--18},

year = {2016},

doi = {10.1007/s00354-016-0005-8},

url = {http://people.idsia.ch/~alessio/decampos-benavoli-ngc2016.pdf}

}

(2016). Learning extended tree augmented naive structures. *International Journal of Approximate Reasoning.* **68**, pp. 153–163.

@ARTICLE{decampos2015a,

title = {Learning extended tree augmented naive structures},

journal = {International Journal of Approximate Reasoning.},

volume = {68},

author = {de Campos, C.P. and Corani, G. and Scanagatta, M. and Cuccu, M. and Zaffalon, M.},

pages = {153--163},

year = {2016},

doi = {10.1016/j.ijar.2015.04.006},

url = {}

}

Downloadtitle = {Learning extended tree augmented naive structures},

journal = {International Journal of Approximate Reasoning.},

volume = {68},

author = {de Campos, C.P. and Corani, G. and Scanagatta, M. and Cuccu, M. and Zaffalon, M.},

pages = {153--163},

year = {2016},

doi = {10.1016/j.ijar.2015.04.006},

url = {}

}

(2016). Air pollution prediction via multi-label classification. *Environmental Modelling & Software* **80**, pp. 259–264.

@ARTICLE{corani2016a,

title = {Air pollution prediction via multi-label classification},

journal = {Environmental Modelling & Software},

volume = {80},

author = {Corani, G. and Scanagatta, M.},

pages = {259--264},

year = {2016},

doi = {10.1016/j.envsoft.2016.02.030},

url = {}

}

Downloadtitle = {Air pollution prediction via multi-label classification},

journal = {Environmental Modelling & Software},

volume = {80},

author = {Corani, G. and Scanagatta, M.},

pages = {259--264},

year = {2016},

doi = {10.1016/j.envsoft.2016.02.030},

url = {}

}

(2016). Hierarchical Bayesian LASSO for a negative binomial regression. *Journal of Statistical Computation and Simulation*.

@ARTICLE{shuaiFu2015a,

title = {Hierarchical {B}ayesian {LASSO} for a negative binomial regression},

journal = {Journal of Statistical Computation and Simulation},

author = {Fu, S.},

year = {2016},

doi = {10.1080/00949655.2015.1106541},

url = {}

}

Downloadtitle = {Hierarchical {B}ayesian {LASSO} for a negative binomial regression},

journal = {Journal of Statistical Computation and Simulation},

author = {Fu, S.},

year = {2016},

doi = {10.1080/00949655.2015.1106541},

url = {}

}

(2016). Computational study of the fluid-dynamics in carotids before and after endarterectomy. *Journal of Biomechanics* **49**(1), pp. 26–38.

@ARTICLE{azzimonti2016a,

title = {Computational study of the fluid-dynamics in carotids before and after endarterectomy},

journal = {Journal of Biomechanics},

editor = {Elsevier},

volume = {49},

author = {Guerciotti, B. and Vergara, C. and Azzimonti, L. and Forzenigo, L. and Buora, A. and Biondetti, P. and Domanin, M.},

number = {1},

pages = {26--38},

year = {2016},

doi = {10.1016/j.jbiomech.2015.11.009},

url = {}

}

Downloadtitle = {Computational study of the fluid-dynamics in carotids before and after endarterectomy},

journal = {Journal of Biomechanics},

editor = {Elsevier},

volume = {49},

author = {Guerciotti, B. and Vergara, C. and Azzimonti, L. and Forzenigo, L. and Buora, A. and Biondetti, P. and Domanin, M.},

number = {1},

pages = {26--38},

year = {2016},

doi = {10.1016/j.jbiomech.2015.11.009},

url = {}

}

(2016). A prior near-ignorance Gaussian process model for nonparametric regression. *International Journal of Approximate Reasoning* **78**, pp. 153–171.

@ARTICLE{mangili2016b,

title = {A prior near-ignorance {G}aussian process model for nonparametric regression},

journal = {International Journal of Approximate Reasoning },

volume = {78},

author = {Mangili, F.},

pages = {153--171},

year = {2016},

doi = {10.1016/j.ijar.2016.07.005},

url = {}

}

Downloadtitle = {A prior near-ignorance {G}aussian process model for nonparametric regression},

journal = {International Journal of Approximate Reasoning },

volume = {78},

author = {Mangili, F.},

pages = {153--171},

year = {2016},

doi = {10.1016/j.ijar.2016.07.005},

url = {}

}

(2016). Adaptive testing by Bayesian networks with application to language assessment. In Micarelli, Alessandro, Stamper, John, Panourgia, Kitty (Eds), *Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings*, Lecture Notes in Computer Science, pp. 471–472.

@INPROCEEDINGS{mangili2016a,

title = {Adaptive testing by {B}ayesian networks with application to language assessment},

editor = {Micarelli, Alessandro and Stamper, John and Panourgia, Kitty},

series = {Lecture Notes in Computer Science},

booktitle = {Intelligent Tutoring Systems: 13th International Conference, {ITS} 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings},

author = {Mangili, F. and Bonesana, C. and Antonucci, A. and Zaffalon, M. and Rubegni, E. and Addimando, L.},

pages = {471--472},

year = {2016},

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url = {http://link.springer.com/content/pdf/bbm%3A978-3-319-39583-8%2F1.pdf}

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editor = {Micarelli, Alessandro and Stamper, John and Panourgia, Kitty},

series = {Lecture Notes in Computer Science},

booktitle = {Intelligent Tutoring Systems: 13th International Conference, {ITS} 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings},

author = {Mangili, F. and Bonesana, C. and Antonucci, A. and Zaffalon, M. and Rubegni, E. and Addimando, L.},

pages = {471--472},

year = {2016},

doi = {},

url = {http://link.springer.com/content/pdf/bbm%3A978-3-319-39583-8%2F1.pdf}

}

(2016). Hidden Markov models with set-valued parameters. *Neurocomputing* **180**, pp. 94–107.

@ARTICLE{antonucci2015c,

title = {Hidden {M}arkov models with set-valued parameters},

journal = {Neurocomputing},

volume = {180},

author = {Mau\'a, D.D. and Antonucci, A. and de Campos, C.P.},

pages = {94--107},

year = {2016},

doi = {doi:10.1016/j.neucom.2015.08.095},

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}

Downloadtitle = {Hidden {M}arkov models with set-valued parameters},

journal = {Neurocomputing},

volume = {180},

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pages = {94--107},

year = {2016},

doi = {doi:10.1016/j.neucom.2015.08.095},

url = {}

}

(2016). Conformity and independence with coherent lower previsions. *International Journal of Approximate Reasoning* **78**, pp. 125–137.

@ARTICLE{zaffalon2016c,

title = {Conformity and independence with coherent lower previsions},

journal = {International Journal of Approximate Reasoning},

volume = {78},

author = {Miranda, E. and Zaffalon, M.},

pages = {125--137},

year = {2016},

doi = {10.1016/j.ijar.2016.07.004},

url = {}

}

Downloadtitle = {Conformity and independence with coherent lower previsions},

journal = {International Journal of Approximate Reasoning},

volume = {78},

author = {Miranda, E. and Zaffalon, M.},

pages = {125--137},

year = {2016},

doi = {10.1016/j.ijar.2016.07.004},

url = {}

}

(2016). Bayesian network data imputation with application to survival tree analysis. *Computational Statistics and Data Analysis* **93**, pp. 373–387.

@ARTICLE{zaffalon2015b,

title = {Bayesian network data imputation with application to survival tree analysis},

journal = {Computational Statistics and Data Analysis},

volume = {93},

author = {Rancoita, P.M.V. and Zaffalon, M. and Zucca, E. and Bertoni, F. and de Campos, C.P.},

pages = {373--387},

year = {2016},

doi = {10.1016/j.csda.2014.12.008},

url = {}

}

Downloadtitle = {Bayesian network data imputation with application to survival tree analysis},

journal = {Computational Statistics and Data Analysis},

volume = {93},

author = {Rancoita, P.M.V. and Zaffalon, M. and Zucca, E. and Bertoni, F. and de Campos, C.P.},

pages = {373--387},

year = {2016},

doi = {10.1016/j.csda.2014.12.008},

url = {}

}

(2016). Learning treewidth-bounded Bayesian networks with thousands of variables. In Daniel D. Lee, Masashi Sugiyama, Ulrike V. Luxburg, Isabelle Guyon, Roman Garnett (Eds), *NIPS 2016: Advances in Neural Information Processing Systems 29* **29**.

@INPROCEEDINGS{scanagatta2016a,

title = {Learning treewidth-bounded {B}ayesian networks with thousands of variables},

journal = {{NIPS}},

editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike V. Luxburg and Isabelle Guyon and Roman Garnett},

volume = {29},

booktitle = {{NIPS} 2016: Advances in Neural Information Processing Systems 29},

author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},

year = {2016},

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url = {http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables}

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Downloadtitle = {Learning treewidth-bounded {B}ayesian networks with thousands of variables},

journal = {{NIPS}},

editor = {Daniel D. Lee and Masashi Sugiyama and Ulrike V. Luxburg and Isabelle Guyon and Roman Garnett},

volume = {29},

booktitle = {{NIPS} 2016: Advances in Neural Information Processing Systems 29},

author = {Scanagatta, M. and Corani, G. and de Campos, C.P. and Zaffalon, M.},

year = {2016},

doi = {},

url = {http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables}

}

top## 2015

## The multilabel naive credal classifier

## Robust classification of multivariate time series by imprecise hidden Markov models

## Early classification of time series by hidden Markov models with set-valued parameters

## Blood flow velocity field estimation via spatial regression with PDE penalization

(2015). The multilabel naive credal classifier. In Augustin, T., Doria, S., Miranda, E., Quaeghebeur, E. (Eds), *ISIPTA '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 27–36.

@INPROCEEDINGS{antonucci2015a,

title = {The multilabel naive credal classifier},

editor = {Augustin, T. and Doria, S. and Miranda, E. and Quaeghebeur, E.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications},

author = {Antonucci, A. and Corani, G.},

pages = {27--36},

year = {2015},

doi = {},

url = {http://www.sipta.org/isipta15/data/paper/32.pdf}

}

Downloadtitle = {The multilabel naive credal classifier},

editor = {Augustin, T. and Doria, S. and Miranda, E. and Quaeghebeur, E.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '15: Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications},

author = {Antonucci, A. and Corani, G.},

pages = {27--36},

year = {2015},

doi = {},

url = {http://www.sipta.org/isipta15/data/paper/32.pdf}

}

(2015). Robust classification of multivariate time series by imprecise hidden Markov models. *International Journal of Approximate Reasoning* **56**(B), pp. 249–263.

@ARTICLE{antonucci2014c,

title = {Robust classification of multivariate time series by imprecise hidden {M}arkov models},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Antonucci, A. and de Rosa, R. and Giusti, A. and Cuzzolin, F.},

number = {B},

pages = {249--263},

year = {2015},

doi = {10.1016/j.ijar.2014.07.005},

url = {}

}

Downloadtitle = {Robust classification of multivariate time series by imprecise hidden {M}arkov models},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Antonucci, A. and de Rosa, R. and Giusti, A. and Cuzzolin, F.},

number = {B},

pages = {249--263},

year = {2015},

doi = {10.1016/j.ijar.2014.07.005},

url = {}

}

(2015). Early classification of time series by hidden Markov models with set-valued parameters. In *Proceedings of the NIPS Time Series Workshop 2015*.

@INPROCEEDINGS{antonucci2015d,

title = {Early classification of time series by hidden {M}arkov models with set-valued parameters},

booktitle = {Proceedings of the {NIPS} Time Series Workshop 2015},

author = {Antonucci, A. and Scanagatta, M. and Mau\`a, D.D. and de Campos, C.P.},

year = {2015},

doi = {},

url = {https://sites.google.com/site/nipsts2015/home}

}

Downloadtitle = {Early classification of time series by hidden {M}arkov models with set-valued parameters},

booktitle = {Proceedings of the {NIPS} Time Series Workshop 2015},

author = {Antonucci, A. and Scanagatta, M. and Mau\`a, D.D. and de Campos, C.P.},

year = {2015},

doi = {},

url = {https://sites.google.com/site/nipsts2015/home}

}

(2015). Blood flow velocity field estimation via spatial regression with PDE penalization. *Journal of the American Statistical Association, Theory and Methods Section* **110**(511), pp. 1057–1071.

@ARTICLE{azzimonti2015a,

title = {Blood flow velocity field estimation via spatial regression with {PDE} penalization},

journal = {Journal of the American Statistical Association, Theory and Methods Section},

volume = {110},

author = {Azzimonti, L. and Sangalli, L.M. and Secchi, P. and Domanin, M. and Nobile, F.},

number = {511},

pages = {1057--1071},

year = {2015},

doi = {10.1080/01621459.2014.946036},

url = {}

}

Downloadtitle = {Blood flow velocity field estimation via spatial regression with {PDE} penalization},

journal = {Journal of the American Statistical Association, Theory and Methods Section},

volume = {110},

author = {Azzimonti, L. and Sangalli, L.M. and Secchi, P. and Domanin, M. and Nobile, F.},

number = {511},

pages = {1057--1071},

year = {2015},

doi = {10.1080/01621459.2014.946036},

url = {}

}

(2015). Variable elimination for interval-valued influence diagrams. In Destercke, S., Denoeux, T. (Eds), *Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015)*, Lecture Notes in Computer Science **9161**, pp. 541–551.