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

## Nonlinear desirability as a linear classification problem

(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 = {}

}

top## 2022

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

## Information algebras in the theory of imprecise probabilities

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

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

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

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

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

## Bounding counterfactuals under selection bias

(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 = {https://doi.org/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 = {https://doi.org/10.1016/j.ijar.2021.11.013},

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). 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.},

year = {2022},

doi = {},

url = {}

}

Downloadtitle = {Modelling assessment rubrics through {B}ayesian networks: a pragmatic approach},

journal = {Proceedings of 2022 International Conference on Software, Telecommunications and Computer Networks ({SoftCOM})},

publisher = {IEEE},

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

year = {2022},

doi = {},

url = {}

}

(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).

@ARTICLE{mejari2022a,

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

journal = {{ASME} Letters in Dynamic Systems and Control},

volume = {2},

author = {Mejari, M. and Piga, D.},

number = {1},

year = {2022},

doi = {10.1115/1.4051491},

url = {https://doi.org/10.1115/1.4051491}

}

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

journal = {{ASME} Letters in Dynamic Systems and Control},

volume = {2},

author = {Mejari, M. and Piga, D.},

number = {1},

year = {2022},

doi = {10.1115/1.4051491},

url = {https://doi.org/10.1115/1.4051491}

}

(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).

@ARTICLE{ijerph19063220,

title = {Risk-based mapping tools for surveillance and control of the invasive mosquito {A}edes albopictus in {S}witzerland},

journal = {International Journal of Environmental Research and Public Health},

volume = {19},

author = {Ravasi, D. and Mangili, F. and Huber, D. and Azzimonti, L. and Engeler, L. and Vermes, N. and Del Rio, G. and Guidi, V. and Tonolla, M. and Flacio, E.},

number = {6},

year = {2022},

doi = {10.3390/ijerph19063220},

url = {https://www.mdpi.com/1660-4601/19/6/3220}

}

Downloadtitle = {Risk-based mapping tools for surveillance and control of the invasive mosquito {A}edes albopictus in {S}witzerland},

journal = {International Journal of Environmental Research and Public Health},

volume = {19},

author = {Ravasi, D. and Mangili, F. and Huber, D. and Azzimonti, L. and Engeler, L. and Vermes, N. and Del Rio, G. and Guidi, V. and Tonolla, M. and Flacio, E.},

number = {6},

year = {2022},

doi = {10.3390/ijerph19063220},

url = {https://www.mdpi.com/1660-4601/19/6/3220}

}

(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},

journal = {{IEEE} Transactions on Computational Imaging},

volume = {8},

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

pages = {133-147},

year = {2022},

doi = {10.1109/TCI.2022.3148062},

url = {}

}

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

journal = {{IEEE} Transactions on Computational Imaging},

volume = {8},

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

pages = {133-147},

year = {2022},

doi = {10.1109/TCI.2022.3148062},

url = {}

}

(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},

editor = {Salmerón, A. and Rumí, R.},

publisher = {JMLR.org},

series = {PMLR},

volume = {186},

booktitle = {Proceedings of {PGM} 2022},

author = {Zaffalon, M. and Antonucci, A. and Cabañas, R. and Huber, D. and Azzimonti, D.},

pages = {289--300},

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url = {https://proceedings.mlr.press/v186/zaffalon22a/zaffalon22a.pdf}

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Downloadtitle = {Bounding counterfactuals under selection bias},

editor = {Salmerón, A. and Rumí, R.},

publisher = {JMLR.org},

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volume = {186},

booktitle = {Proceedings of {PGM} 2022},

author = {Zaffalon, M. and Antonucci, A. and Cabañas, R. and Huber, D. and Azzimonti, D.},

pages = {289--300},

year = {2022},

doi = {},

url = {https://proceedings.mlr.press/v186/zaffalon22a/zaffalon22a.pdf}

}

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

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

## 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

## Using data-driven bayesian network analysis to explore recovery pathways in people with low back pain receiving individualised physiotherapy or advice

## 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

## 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

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

## 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)*.

@INPROCEEDINGS{antonucci2021e,

title = {Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption},

booktitle = {4th Workshop on Tractable Probabilistic Modeling ({TPM} 2021 {c}o-{l}ocated {w}ith {UAI} 2021)},

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

year = {2021},

doi = {},

url = {https://arxiv.org/abs/2107.12130}

}

Downloadtitle = {Structural learning of probabilistic sentential decision diagrams under partial closed-world assumption},

booktitle = {4th Workshop on Tractable Probabilistic Modeling ({TPM} 2021 {c}o-{l}ocated {w}ith {UAI} 2021)},

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

year = {2021},

doi = {},

url = {https://arxiv.org/abs/2107.12130}

}

(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.

@INPROCEEDINGS{antonucci2021c,

title = {A new score for adaptive tests in {B}ayesian and credal networks},

editor = {Vejnarov\'a, J. and Wilson, N.},

publisher = {Springer International Publishing},

address = {Cham},

booktitle = {Symbolic and Quantitative Approaches to Reasoning With Uncertainty},

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

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year = {2021},

doi = {https://doi.org/10.1007/978-3-030-86772-0_29},

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Downloadtitle = {A new score for adaptive tests in {B}ayesian and credal networks},

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

pages = {399--412},

year = {2021},

doi = {https://doi.org/10.1007/978-3-030-86772-0_29},

url = {}

}

(2021). Angrybert: joint learning target and emotion for hate speech detection. In Karlapalem, K., Cheng, H., Ramakrishnan, N., Agrawal, R.K., Reddy, P.K., Srivastava, J., Chakraborty, T. (Eds), *Advances in Knowledge Discovery and Data Mining - 25th Pacific-asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I*, Lecture Notes in Computer Science **12712**, Springer, pp. 701–713.

@INPROCEEDINGS{mitrovic2021a,

title = {Angrybert: joint learning target and emotion for hate speech detection},

editor = {Karlapalem, K. and Cheng, H. and Ramakrishnan, N. and Agrawal, R.K. and Reddy, P.K. and Srivastava, J. and Chakraborty, T.},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

volume = {12712},

booktitle = {Advances in Knowledge Discovery and Data Mining - 25th Pacific-{a}sia Conference, {PAKDD} 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I},

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Downloadtitle = {Angrybert: joint learning target and emotion for hate speech detection},

editor = {Karlapalem, K. and Cheng, H. and Ramakrishnan, N. and Agrawal, R.K. and Reddy, P.K. and Srivastava, J. and Chakraborty, T.},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

volume = {12712},

booktitle = {Advances in Knowledge Discovery and Data Mining - 25th Pacific-{a}sia Conference, {PAKDD} 2021, Virtual Event, May 11-14, 2021, Proceedings, Part I},

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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.

@ARTICLE{azzimontid2021,

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

journal = {{IEEE/OSA} Journal of Optical Communications and Networking},

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

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number = {1},

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year = {2021},

doi = {10.1364/JOCN.401918},

url = {}

}

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

@ARTICLE{piga2021a,

title = {Global optimization based on active preference learning with radial basis functions},

journal = {Machine Learning},

publisher = {Springer},

volume = {110},

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

pages = {417--448},

year = {2021},

doi = {10.1007/s10994-020-05935-y},

url = {}

}

Downloadtitle = {Global optimization based on active preference learning with radial basis functions},

journal = {Machine Learning},

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

pages = {417--448},

year = {2021},

doi = {10.1007/s10994-020-05935-y},

url = {}

}

(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, 1842–1850.

@INPROCEEDINGS{azzimontid2021b,

title = {Preferential bayesian optimisation with skew gaussian processes},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {GECCO '21},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},

author = {Benavoli, A. and Azzimonti, D. and Piga, D.},

pages = {1842--1850},

year = {2021},

doi = {10.1145/3449726.3463128},

url = {}

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Downloadtitle = {Preferential bayesian optimisation with skew gaussian processes},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {GECCO '21},

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author = {Benavoli, A. and Azzimonti, D. and Piga, D.},

pages = {1842--1850},

year = {2021},

doi = {10.1145/3449726.3463128},

url = {}

}

(2021). A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew gaussian processes. *Machine Learning* **110**(11), 3095–3133.

@ARTICLE{azzimontid2021c,

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

journal = {Machine Learning},

volume = {110},

author = {Benavoli, A. and Azzimonti, D. and Piga, D.},

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pages = {3095--3133},

year = {2021},

doi = {10.1007/s10994-021-06039-x},

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

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volume = {110},

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number = {11},

pages = {3095--3133},

year = {2021},

doi = {10.1007/s10994-021-06039-x},

url = {}

}

(2021). State Space approximation of Gaussian Processes for time-series forecasting. In *Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th ECML PKDD Workshop, AALTD 2021*.

@INPROCEEDINGS{corani2021b,

title = {State {S}pace approximation of {G}aussian {P}rocesses for time-series forecasting},

booktitle = {Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th {ECML} {PKDD} Workshop, {AALTD} 2021},

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

year = {2021},

doi = {},

url = {https://project.inria.fr/aaltd21/accepted-papers/}

}

Downloadtitle = {State {S}pace approximation of {G}aussian {P}rocesses for time-series forecasting},

booktitle = {Proc. Workshop on Advanced Analytics and Learning on Temporal Data, 6th {ECML} {PKDD} Workshop, {AALTD} 2021},

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

year = {2021},

doi = {},

url = {https://project.inria.fr/aaltd21/accepted-papers/}

}

(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).

@ARTICLE{azzimonti2021b,

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

journal = {Journal of Personalized Medicine},

volume = {11},

author = {Berjano, P. and Langella, F. and Ventriglia, L. and Compagnone, D. and Barletta, P. and Huber, D. and Mangili, F. and Licandro, G. and Galbusera, F. and Cina, A. and Bassani, T. and Lamartina, C. and Scaramuzzo, L. and Bassani, R. and Brayda-Bruno, M. and Villafa\~ne, J.H. and Monti, L. and Azzimonti, L.},

number = {12},

year = {2021},

doi = {10.3390/jpm11121377},

url = {}

}

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

journal = {Journal of Personalized Medicine},

volume = {11},

author = {Berjano, P. and Langella, F. and Ventriglia, L. and Compagnone, D. and Barletta, P. and Huber, D. and Mangili, F. and Licandro, G. and Galbusera, F. and Cina, A. and Bassani, T. and Lamartina, C. and Scaramuzzo, L. and Bassani, R. and Brayda-Bruno, M. and Villafa\~ne, J.H. and Monti, L. and Azzimonti, L.},

number = {12},

year = {2021},

doi = {10.3390/jpm11121377},

url = {}

}

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

@ARTICLE{piga2021c,

title = {Model structure selection for switched narx system identification: a randomized approach},

journal = {Automatica},

volume = {125},

author = {Bianchi, F. and Breschi, V. and Piga, D. and Piroddi, L.},

year = {2021},

doi = {https://doi.org/10.1016/j.automatica.2020.109415},

url = {https://www.sciencedirect.com/science/article/pii/S0005109820306178}

}

Downloadtitle = {Model structure selection for switched narx system identification: a randomized approach},

journal = {Automatica},

volume = {125},

author = {Bianchi, F. and Breschi, V. and Piga, D. and Piroddi, L.},

year = {2021},

doi = {https://doi.org/10.1016/j.automatica.2020.109415},

url = {https://www.sciencedirect.com/science/article/pii/S0005109820306178}

}

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

@ARTICLE{azzimonti2021a,

title = {Artificial intelligence in thyroid field. A comprehensive review},

journal = {Cancers},

volume = {13},

author = {Bini, F. and Pica, A. and Azzimonti, L. and Giusti, A. and Ruinelli, L. and Marinozzi, F. and Trimboli, P.},

number = {19},

year = {2021},

doi = {10.3390/cancers13194740},

url = {https://www.mdpi.com/2072-6694/13/19/4740}

}

Downloadtitle = {Artificial intelligence in thyroid field. A comprehensive review},

journal = {Cancers},

volume = {13},

author = {Bini, F. and Pica, A. and Azzimonti, L. and Giusti, A. and Ruinelli, L. and Marinozzi, F. and Trimboli, P.},

number = {19},

year = {2021},

doi = {10.3390/cancers13194740},

url = {https://www.mdpi.com/2072-6694/13/19/4740}

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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. 61-71.

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(2021). Algebras of sets and coherent sets of gambles. In *European Conference on Symbolic and Quantitative Approaches with Uncertainty*, 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*.

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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). Enhancing Biomedical Relation Extraction with Transformer Models using Shortest Dependency Path Features and Triplet Information. *Journal of Biomedical Informatics*.

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(2021). Information algebras of coherent sets of gambles in general possibility spaces. In *Proceedings of Machine Learning Research* **147**, 191–200.

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(2021). Using data-driven bayesian network analysis to explore recovery pathways in people with low back pain receiving individualised physiotherapy or advice. *PLoS ONE* **16**(10), e0258515.

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(2021). Mechanisms of recovery after neck-specific or general exercises in patients with cervical radiculopathy. *European Journal of Pain* **25**(5), pp. 1162–1172.

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(2021). Cautious classification with data missing not at random using generative random forests. In Vejnarová, J., Wilson, N. (Eds), *Symbolic and Quantitative Approaches to Reasoning With Uncertainty*, Springer International Publishing, Cham, pp. 284–298.

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(2021). Probabilistic models with deep neural networks. *Entropy* **23**(1).

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(2021). An integral architecture for identification of continuous-time state-space lpv models. In *4th IFAC Workshop on Linear Parameter-Varying Systems LPVS 2021*, Milan, Italy.

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(2021). Relation clustering in narrative knowledge graphs. In *AI4Narratives Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 20)*.

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(2021). A model-agnostic algorithm for bayes error determination in binary classification. *Algorithms* **14**(11).

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(2021). Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks. *Autonomous Robots*.

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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

## 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

## 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

## 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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pages = {M4E.1},

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(2020). Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. In Jaeger, M., Nielsen, T.D. (Eds), *Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020)*, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.

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(2020). A bias-correction approach for the identification of piecewise affine output-error models. In *21st IFAC World Congress (IFAC 2020)*, Berlin, Germany.

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(2020). Recursive bias-correction method for identification of piecewise affine output-error models. *IEEE Control Systems Letters* **4**, pp. 970–975.

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

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

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

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

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(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*.

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

@ARTICLE{roveda2020a,

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

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

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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*.

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(2020). Model-based reinforcement learning variable impedance control for human-robot collaboration. *Journal of Intelligent & Robotic Systems*.

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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*.

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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*.

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

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

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

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

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

booktitle = {{IEEE} International Conference on Systems, Man, and Cybernetics},

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year = {2020},

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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.

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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)*.

@INPROCEEDINGS{Roveda2020i,

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

booktitle = {2020 {IEEE} International Workshop on Metrology for Industry 4.0 & {IoT} ({MetroInd4}.0 & {IoT})},

author = {Sorgini, F. and Air\`o Farulla, G. and Lukic, N. and Danilov, I. and Roveda, L. and Milivojevic, M. and Babu Pulikottil, T. and Carrozza, M.C. and Prinetto, P. and Tolio, T. and Oddo, C.M. and Petrovic, P. and Bojovic, B.},

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year = {2020},

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

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

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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)*.

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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, 581–592.

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top## 2019

## Reliable discretisation of deterministic equations in Bayesian networks

## Credal sentential decision diagrams

## Modeling spatially dependent functional data via regression with differential regularization

## Hierarchical estimation of parameters in Bayesian networks

## Adaptive design of experiments for conservative estimation of excursion sets

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

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

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

## On minimum elementary-triplet bases for independence relations

## Online end-use energy disaggregation via jump linear models

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

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

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

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

## An experimental study of prior dependence in Bayesian network structure learning

## Approaching SMM4H with merged models and multi-task learning

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

## Oger++: hybrid multi-type entity recognition

## NOVEL2GRAPH: Visual summaries of narrative text enhanced by machine learning

## Semantically corroborating neural attention for biomedical question answering

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

## On intercausal interactions in probabilistic relational models

## Exploring the space of probabilistic sentential decision diagrams

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

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

## Compatibility, coherence and the RIP

## A tutorial on machine learning for failure management in optical networks

## Reverse engineering creativity into interpretable neural networks

## Incremental alignment of metaphoric language model for poetry composition

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

## Semialgebraic outer approximations for set-valued nonlinear filtering

## Performance-oriented model learning for data-driven MPC design

## The hidden elegance of causal interaction models

## Revisiting the decay of scientific email addresses

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

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

## Efficient feature selection using shrinkage estimators

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

## A new approach and gold standard toward author disambiguation in MEDLINE

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

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

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

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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). 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). 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 *BioASQ: Large-Scale Biomedical Semantic Indexing and Question Answering: Workshop of ECML/PKDD 2019*, Springer, Lecture Notes in Computer Science.

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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). 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). 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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Downloadtitle = {Mechanical and control design of an industrial exoskeleton for advanced human empowering in heavy parts manipulation tasks},

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author = {Mauri, A. and Lettori, J. and Fusi, G. and Fausti, D. and Mor, M. and Braghin, F. and Legnani, G. and Roveda, L.},

year = {2019},

doi = {10.3390/robotics8030065},

url = {}

}

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

@INPROCEEDINGS{piga2019e,

title = {Kernelized identification of linear parameter-varying models with linear fractional representation},

address = {Naples, Italy},

booktitle = {2019 European Control Conference ({e}cc)},

author = {Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.},

year = {2019},

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}

Downloadtitle = {Kernelized identification of linear parameter-varying models with linear fractional representation},

address = {Naples, Italy},

booktitle = {2019 European Control Conference ({e}cc)},

author = {Mejari, M. and Piga, D. and Toth, R. and Bemporad, A.},

year = {2019},

doi = {10.23919/ECC.2019.8796150},

url = {}

}

(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,

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series = {Advances in Intelligent Systems and Computing},

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

@ARTICLE{corani2019b,

title = {A tutorial on machine learning for failure management in optical networks},

journal = {Journal of Lightwave Technology},

volume = {37},

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year = {2019},

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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.

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pages = {235-247},

year = {2019},

doi = {10.1007/978-3-030-12385-7_19},

url = {}

}

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

@INPROCEEDINGS{oita2019poetryComposition,

title = {Incremental alignment of metaphoric language model for poetry composition},

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}

(2019). Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators. *International Journal of Control*, pp. 1–14.

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title = {Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators},

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year = {2019},

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

@INPROCEEDINGS{piga2019f,

title = {Semialgebraic outer approximations for set-valued nonlinear filtering},

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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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year = {2019},

doi = {10.1109/LCSYS.2019.2913347},

url = {}

}

(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.

@INPROCEEDINGS{linda2019a,

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volume = {11940},

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}

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

@ARTICLE{rinaldi2019j,

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

journal = {{bioRxiv}},

publisher = {Cold Spring Harbor Laboratory},

author = {Rodriguez-Esteban, R. and Vishnyakova, D. and Rinaldi, F.},

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

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year = {2019},

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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},

journal = {Frontiers in Robotics and {AI}},

volume = {6},

author = {Roveda, L. and Haghshenas, S. and Caimmi, M. and Pedrocchi, N. and Molinari Tosatti, L.},

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

journal = {Frontiers in Robotics and {AI}},

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pages = {75},

year = {2019},

doi = {10.3389/frobt.2019.00075},

url = {}

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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},

journal = {Computers & Operations Research},

volume = {108},

author = {Salani, M. and Corbellini, G. and Corani, G.},

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

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}

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

journal = {Computers & Operations Research},

volume = {108},

author = {Salani, M. and Corbellini, G. and Corani, G.},

pages = {112--120},

year = {2019},

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

url = {}

}

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

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title = {Efficient feature selection using shrinkage estimators},

journal = {Machine Learning},

volume = {108},

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

number = {8},

pages = {1261--1286},

year = {2019},

doi = {10.1007/s10994-019-05795-1},

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}

Downloadtitle = {Efficient feature selection using shrinkage estimators},

journal = {Machine Learning},

volume = {108},

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

number = {8},

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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},

volume = {7},

author = {Sheikhalishahi, S. and Miotto, R. and Dudley, J.T. and Lavelli, A. and Rinaldi, F. and Osmani, V.},

number = {2},

pages = {e12239},

year = {2019},

doi = {10.2196/12239},

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

journal = {{JMIR} Med Inform},

volume = {7},

author = {Sheikhalishahi, S. and Miotto, R. and Dudley, J.T. and Lavelli, A. and Rinaldi, F. and Osmani, V.},

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pages = {e12239},

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doi = {10.2196/12239},

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.

@ARTICLE{rinaldi2019a,

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

journal = {J Am Med Inform Assoc},

volume = {26},

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number = {10},

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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

## 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)},

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}

}

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

@INPROCEEDINGS{antonucci2018d,

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

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

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

year = {2018},

doi = {},

url = {http://stoics.org.uk/plp/plp2018/}

}

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

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

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

year = {2018},

doi = {},

url = {http://stoics.org.uk/plp/plp2018/}

}

(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},

url = {}

}

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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year = {2018},

doi = {10.1109/CDC.2018.8619819},

url = {}

}

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

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

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

pages = {2247--2252},

year = {2018},

doi = {10.1109/CDC.2018.8619819},

url = {}

}

(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.},

number = {5},

pages = {108--113},

year = {2018},

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

url = {}

}

Downloadtitle = {Kalman filtering for energy disaggregation},

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

volume = {51},

booktitle = {Proc. {o}f the 1st {IFAC} Workshop on Integrated Assessment Modelling for Environmental Systems},

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

number = {5},

pages = {108--113},

year = {2018},

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

url = {}

}

(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},

url = {}

}

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},

url = {}

}

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). 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 = { https://aaai.org/ocs/index.php/FLAIRS/FLAIRS18/paper/download/17696/16792}

}

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 = { https://aaai.org/ocs/index.php/FLAIRS/FLAIRS18/paper/download/17696/16792}

}

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

@INPROCEEDINGS{antonucci2018b,

title = {Imaginary kinematics},

publisher = {AUAI Press},

booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence},

author = {Marchetti, S. and Antonucci, A.},

pages = {104-113},

year = {2018},

doi = {},

url = {http://auai.org/uai2018/proceedings/papers/42.pdf}

}

Downloadtitle = {Imaginary kinematics},

publisher = {AUAI Press},

booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence},

author = {Marchetti, S. and Antonucci, A.},

pages = {104-113},

year = {2018},

doi = {},

url = {http://auai.org/uai2018/proceedings/papers/42.pdf}

}

(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.

@INPROCEEDINGS{piga2018d,

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

volume = {51},

booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},

author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},

number = {15},

pages = {1092--1097},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.048},

url = {}

}

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

volume = {51},

booktitle = {Proc. {o}f the 18th {IFAC} Symposium on System Identification},

author = {Mejari, M. and Naik, V.V. and Piga, D. and Bemporad, A.},

number = {15},

pages = {1092--1097},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.048},

url = {}

}

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

@INPROCEEDINGS{piga2018h,

title = {Energy disaggregation using piecewise affine regression and binary quadratic programming},

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

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year = {2018},

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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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volume = {87},

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Downloadtitle = {A bias-correction method for closed-loop identification of linear parameter-varying systems},

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year = {2018},

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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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journal = {{IEEE} Transactions on Control Systems Technology},

volume = {26},

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volume = {26},

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number = {4},

pages = {1422--1429},

year = {2018},

doi = {10.1109/TCST.2017.2702118},

url = {}

}

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

@ARTICLE{scanagatta2018b,

title = {Approximate structure learning for large {B}ayesian networks},

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number = {8-10},

pages = {1209--1227},

year = {2018},

doi = {10.1007/s10994-018-5701-9},

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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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year = {2018},

doi = {10.1016/j.ijar.2018.02.004},

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}

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

@INPROCEEDINGS{piga2018g,

title = {Towards direct data-driven model-free design of optimal controllers},

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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. },

publisher = {Springer International Publishing},

booktitle = {Functional Statistics and Related Fields},

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

pages = {41--44},

year = {2017},

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

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}

Downloadtitle = {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.},

pages = {41--44},

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},

editor = {Raghavan, V., Aluru, S., Karypis, G., Miele, L., Wu, X.},

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

author = {Azzimonti, L. and Corani, G. and Zaffalon, M.},

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pages = {739--744},

year = {2017},

doi = {10.1109/ICDM.2017.85},

url = {}

}

(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},

journal = {Computer Science - Research and Development},

volume = {33},

author = {Bucher, D. and Mangili, F. and Bonesana, C. and Jonietz, D. and Cellina, F. and Raubal, M.},

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number = {1},

pages = {1-2},

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},

journal = {Machine Learning},

volume = {106},

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

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

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number = {11},

pages = {1817--1837},

year = {2017},

doi = {10.1007/s10994-017-5641-9},

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}

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

@ARTICLE{mangili2017a,

title = {Profiling the location and extent of musicians' pain using digital pain drawings},

journal = {{PAIN} Practice},

publisher = {Wiley},

volume = {18},

author = {Cruder, C. and Falla, D. and Mangili, F. and Azzimonti, L. and Ara\'ujo, L. and Williamon, A. and Barbero, M.},

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}

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

@ARTICLE{azzimonti2017a,

title = {Invariant {NKT} cells contribute to {C}hronic {L}ymphocytic {L}eukemia surveillance and prognosis},

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volume = {129},

author = {Gorini, F. and Azzimonti, L. and Delfanti, G. and Scarf\`o, L. and Scielzo, C. and Bertilaccio, M.T. and Ranghetti, P. and Gulino, A. and Doglioni, C. and Napoli, A.D. and Capri, M. and Franceschi, C. and Calligaris-Cappio, F. and Ghia, P. and Bellone, M. and Dellabona, P. and Casorati, G. and de Lalla, C.},

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number = {26},

pages = {3440-3451},

year = {2017},

doi = {10.1182/blood-2016-11-751065},

url = {}

}

(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.

@INPROCEEDINGS{mangili2017b,

title = {Reliable knowledge-based adaptive tests by credal networks},

editor = {Antonucci, A. and Cholvy, L. and Papini, O.},

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series = {Lecture Notes in Computer Science},

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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).

@ARTICLE{piga2017a,

title = {A unified framework for deterministic and probabilistic d-stability analysis of uncertain polynomial matrices},

journal = {{IEEE} Transactions on Automatic Control},

volume = {PP},

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year = {2017},

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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.

@INPROCEEDINGS{scanagatta2017,

title = {Improved local search in {B}ayesian networks structure learning},

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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},

url = {}

}

Downloadtitle = {Evaluating interval-valued influence diagrams},

journal = {International Journal of Approximate Reasoning},

volume = {80},

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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}

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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},

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pages = {153--163},

year = {2016},

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

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}

(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},

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

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}

(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},

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}

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

journal = {Journal of Statistical Computation and Simulation},

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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},

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}

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

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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*.

@ARTICLE{mangili2016b,

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

journal = {International Journal of Approximate Reasoning },

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year = {2016},

doi = {http://dx.doi.org/10.1016/j.ijar.2016.07.005},

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}

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

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author = {Mangili, F.},

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doi = {http://dx.doi.org/10.1016/j.ijar.2016.07.005},

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}

(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.

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

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

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

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year = {2016},

doi = {},

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

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(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.},

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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},

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year = {2016},

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

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}

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journal = {International Journal of Approximate Reasoning},

volume = {78},

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year = {2016},

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

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}

(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},

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pages = {373--387},

year = {2016},

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

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}

(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*.

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

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year = {2016},

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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

## Variable elimination for interval-valued influence diagrams

## Imprecision in machine learning and AI

## A Bayesian approach for comparing cross-validated algorithms on multiple data sets

## Bayesian hypothesis testing in machine learning

## Credal model averaging for classification: representing prior ignorance and expert opinions.

## Robust Bayesian model averaging for the analysis of presence–absence data

## A hierarchical Bayesian approach to negative binomial regression

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

## New prior near-ignorance models on the simplex

## Reliable survival analysis based on the Dirichlet Process

## On the problem of computing the conglomerable natural extension

## Independent products in infinite spaces

## Conformity and independence with coherent lower previsions

## Learning Bayesian networks with thousands of variables

(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.},

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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},

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Downloadtitle = {The multilabel naive credal classifier},

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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},

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}

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

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volume = {56},

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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.},

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

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

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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},

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}

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

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number = {511},

pages = {1057--1071},

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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.

@INCOLLECTION{antonucci2015b,

title = {Variable elimination for interval-valued influence diagrams},

editor = {Destercke, S. and Denoeux, T.},

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booktitle = {Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty ({ECSQARU} 2015)},

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

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year = {2015},

chapter = {Symbolic and Quantitative Approaches to Reasoning with Uncertainty},

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}

(2015). Imprecision in machine learning and AI. In *The IEEE Intelligent Informatics Bulletin* **16**(1), IEEE Computer Society, pp. 20–23.

@INCOLLECTION{antonucci2015e,

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(2015). A Bayesian approach for comparing cross-validated algorithms on multiple data sets. *Machine Learning* **100**(2), pp. 285–304.

@ARTICLE{corani2015b,

title = {A {B}ayesian approach for comparing cross-validated algorithms on multiple data sets},

journal = {Machine Learning},

volume = {100},

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year = {2015},

doi = {10.1007/s10994-015-5486-z},

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year = {2015},

doi = {10.1007/s10994-015-5486-z},

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(2015). Bayesian hypothesis testing in machine learning. In *Proc. ECML PKDD 2015 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases)*, pp. 199–202.

@INPROCEEDINGS{corani2015c,

title = {Bayesian hypothesis testing in machine learning},

booktitle = {Proc. {ECML} {PKDD} 2015 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases)},

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Downloadtitle = {Bayesian hypothesis testing in machine learning},

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year = {2015},

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(2015). Credal model averaging for classification: representing prior ignorance and expert opinions.. *International Journal of Approximate Reasoning* **56**(B), pp. 264–277.

@ARTICLE{corani2014a,

title = {Credal model averaging for classification: representing prior ignorance and expert opinions.},

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Downloadtitle = {Credal model averaging for classification: representing prior ignorance and expert opinions.},

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year = {2015},

doi = {10.1016/j.ijar.2014.07.001},

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}

(2015). Robust Bayesian model averaging for the analysis of presence–absence data. *Environmental and Ecological Statistics* **22**(3), pp. 513–534.

@ARTICLE{corani2015a,

title = {Robust {B}ayesian model averaging for the analysis of presence--absence data},

journal = {Environmental and Ecological Statistics},

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year = {2015},

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(2015). A hierarchical Bayesian approach to negative binomial regression. *Methods and Applications of Analysis* **22**(4), pp. 409–428.

@ARTICLE{shuaiFu2015b,

title = {A hierarchical {B}ayesian approach to negative binomial regression},

journal = {Methods and Applications of Analysis},

volume = {22},

author = {Fu, S.},

number = {4},

pages = {409--428},

year = {2015},

doi = {10.4310/MAA.2015.v22.n4.a4},

url = {}

}

Downloadtitle = {A hierarchical {B}ayesian approach to negative binomial regression},

journal = {Methods and Applications of Analysis},

volume = {22},

author = {Fu, S.},

number = {4},

pages = {409--428},

year = {2015},

doi = {10.4310/MAA.2015.v22.n4.a4},

url = {}

}

(2015). A prior near-ignorance Gaussian Process model for nonparametric regression. 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. 187–196.

@INPROCEEDINGS{mangili2015b,

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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},

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url = {http://www.sipta.org/isipta15/data/paper/15.pdf}

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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 = {Mangili, F.},

pages = {187--196},

year = {2015},

doi = {},

url = {http://www.sipta.org/isipta15/data/paper/15.pdf}

}

(2015). New prior near-ignorance models on the simplex. *International Journal of Approximate Reasoning* **56**(Part B), pp. 278–306.

@ARTICLE{Mangili2014a,

title = {New prior near-ignorance models on the simplex},

journal = {International Journal of Approximate Reasoning},

volume = {56},

author = {Mangili, F. and Benavoli, A.},

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pages = {278--306},

year = {2015},

doi = {10.1016/j.ijar.2014.08.005},

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}

Downloadtitle = {New prior near-ignorance models on the simplex},

journal = {International Journal of Approximate Reasoning},

volume = {56},

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year = {2015},

doi = {10.1016/j.ijar.2014.08.005},

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(2015). Reliable survival analysis based on the Dirichlet Process. *Biometrical Journal* **57**(6), pp. 1002–1019.

@ARTICLE{mangili2015a,

title = {Reliable survival analysis based on the {D}irichlet {P}rocess},

journal = {Biometrical Journal},

volume = {57},

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number = {6},

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(2015). On the problem of computing the conglomerable natural extension. *International Journal of Approximate Reasoning* **56**(A), pp. 1–27.

@ARTICLE{zaffalon2014b,

title = {On the problem of computing the conglomerable natural extension},

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}

(2015). Independent products in infinite spaces. *Journal of Mathematical Analysis and Applications* **425**(1), pp. 460–488.

@ARTICLE{zaffalon2015a,

title = {Independent products in infinite spaces},

journal = {Journal of Mathematical Analysis and Applications},

volume = {425},

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

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doi = {10.1016/j.jmaa.2014.12.049},

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number = {1},

pages = {460--488},

year = {2015},

doi = {10.1016/j.jmaa.2014.12.049},

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(2015). Conformity and independence with coherent lower previsions. 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. 197–206.

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(2015). Learning Bayesian networks with thousands of variables. In Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, Roman Garnett (Eds), *NIPS 2015: Advances in Neural Information Processing Systems 28*.

@INPROCEEDINGS{scanagatta2015a,

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

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top## 2014

## Approximate credal network updating by linear programming with applications to decision making

## Probabilistic graphical models

## Decision making with hierarchical credal sets

## Mixed finite elements for spatial regression with PDE penalization

## Global sensitivity analysis for MAP inference in graphical models

## Extended tree augmented naive classifier

## Classification

## Credal Ensembles of Classifiers

## Trading off Speed and Accuracy in Multilabel Classification

## Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms

## Hidden Markov models with imprecisely specified parameters

## Probabilistic inference in credal networks: new complexity results

## Transform both sides model: a parametric approach

## Min-BDeu and max-BDeu scores for learning Bayesian networks

## Comments on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks" by Andrés R. Masegosa and Serafín Moral

(2014). Approximate credal network updating by linear programming with applications to decision making. *International Journal of Approximate Reasoning* **58**, pp. 25–38.

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(2014). Probabilistic graphical models. In Augustin, T., Coolen, F., de Cooman, G., Troffaes, M. (Eds), *Introduction to Imprecise Probabilities*, Wiley, pp. 207–229.

@INBOOK{antonucci2014a,

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author = {Antonucci, A. and de Campos, C.P. and Zaffalon, M.},

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Downloadtitle = {Probabilistic graphical models},

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pages = {207--229},

year = {2014},

chapter = {9},

doi = {10.1002/9781118763117.ch9},

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(2014). Decision making with hierarchical credal sets. In Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (Eds), *Information Processing and Management of Uncertainty in Knowledge-based Systems*, Communications in Computer and Information Science **444**, Springer, pp. 456–465.

@INPROCEEDINGS{antonucci2014b,

title = {Decision making with hierarchical credal sets},

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series = {Communications in Computer and Information Science},

volume = {444},

booktitle = {Information Processing and Management of Uncertainty in Knowledge-{b}ased Systems},

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year = {2014},

doi = {10.1007/978-3-319-08852-5_47},

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(2014). Mixed finite elements for spatial regression with PDE penalization. *SIAM/ASA Journal on Uncertainty Quantification* **2**(1), pp. 305-335.

@ARTICLE{azzimonti2014a,

title = {Mixed finite elements for spatial regression with {PDE} penalization},

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volume = {2},

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number = {1},

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year = {2014},

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(2014). Global sensitivity analysis for MAP inference in graphical models. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D. , Weinberger, K.Q. (Eds), *Advances in Neural Information Processing Systems 27 (NIPS 2014)*, Curran Associates, Inc., pp. 2690–2698.

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(2014). Extended tree augmented naive classifier. In van der Gaag, L., Feelders, A. (Ed), *PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models*, Lecture Notes in Artificial Intelligence **8754**, Springer, pp. 176–189.

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(2014). Classification. In Augustin,T., Coolen,F., de Cooman,G., Troffaes,M. (Eds), *Introduction to Imprecise Probabilities*, Wiley, pp. 261–285.

@INCOLLECTION{corani2013b,

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(2014). Credal Ensembles of Classifiers. *Computational Statistics & Data Analysis* **71**, pp. 818–831.

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title = {Credal {E}nsembles of {C}lassifiers},

journal = {Computational Statistics & Data Analysis},

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Downloadtitle = {Credal {E}nsembles of {C}lassifiers},

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(2014). Trading off Speed and Accuracy in Multilabel Classification. In van der Gaag, L., Feelders, A. (Eds), *PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models*, Lecture Notes in Artificial Intelligence **8754**, Springer, pp. 145–159.

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(2014). Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms. *International Journal of Approximate Reasoning* **55**(2), pp. 666–682.

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(2014). Hidden Markov models with imprecisely specified parameters. In *Proceedings of the Brazilian Conference on Intelligent Systems*.

@INPROCEEDINGS{antonucci2014d,

title = {Hidden {M}arkov models with imprecisely specified parameters},

booktitle = {Proceedings of the Brazilian Conference on Intelligent Systems},

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Downloadtitle = {Hidden {M}arkov models with imprecisely specified parameters},

booktitle = {Proceedings of the Brazilian Conference on Intelligent Systems},

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(2014). Probabilistic inference in credal networks: new complexity results. *Journal of Artifical Intelligence Research* **50**, pp. 603–637.

@ARTICLE{maua14jair,

title = {Probabilistic inference in credal networks: new complexity results},

journal = {Journal of Artifical Intelligence Research},

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(2014). Transform both sides model: a parametric approach. *Computational Statistics and Data Analysis* **71**, pp. 903–913.

@ARTICLE{decampos2013b,

title = {Transform both sides model: a parametric approach},

journal = {Computational Statistics and Data Analysis},

publisher = {Elsevier},

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Downloadtitle = {Transform both sides model: a parametric approach},

journal = {Computational Statistics and Data Analysis},

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doi = {10.1016/j.csda.2013.07.023},

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(2014). Min-BDeu and max-BDeu scores for learning Bayesian networks. In van der Gaag, L., Feelders, A. (Eds), *PGM'14: Proceedings of the Seventh European Workshop on Probabilistic Graphical Models*, Lecture Notes in Artificial Intelligence **8754**, Springer, pp. 426–441.

@INPROCEEDINGS{scanagatta2014a,

title = {Min-{BDeu} and max-{BDeu} scores for learning {B}ayesian networks},

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(2014). Comments on "Imprecise probability models for learning multinomial distributions from data. Applications to learning credal networks" by Andrés R. Masegosa and Serafín Moral. *International Journal of Approximate Reasoning* **55**(7), pp. 1597–1600.

@ARTICLE{zaffalon2014a,

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top## 2013

## Approximating credal network inferences by linear programming

## An ensemble of Bayesian networks for multilabel classification

## CREDO: a military decision-support system based on credal networks

## Temporal data classification by imprecise dynamical models

## Nonlinear nonparametric mixed-effects models for unsupervised classification

## Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks

## Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices

## A Bayesian network model for predicting pregnancy after in vitro fertilization

## Credal model averaging of logistic regression for modeling the distribution of marmot burrows

## Objective way to support embryo transfer: a probabilistic decision

## Prognostic impact of monocyte count at presentation in mantle cell lymphoma

## New prior near-ignorance models on the simplex

## On the complexity of strong and epistemic credal networks

## On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables

## Conglomerable coherent lower previsions

## Conglomerable coherence

## Computing the conglomerable natural extension

## Probability and time

(2013). Approximating credal network inferences by linear programming. In van der Gaag, L. C. (Ed), *Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty*, Lecture Notes in Artificial Intelligence **7958**, Springer, Berlin Heidelberg, pp. 13–25.

@INPROCEEDINGS{antonucci2013a,

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editor = {van der Gaag, L. C.},

publisher = {Springer},

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series = {Lecture Notes in Artificial Intelligence},

volume = {7958},

booktitle = {Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty},

author = {Antonucci, A. and de Campos, C.P. and Huber, D. and Zaffalon, M.},

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doi = {10.1007/978-3-642-39091-3_2},

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Downloadtitle = {Approximating credal network inferences by linear programming},

editor = {van der Gaag, L. C.},

publisher = {Springer},

address = {Berlin Heidelberg},

series = {Lecture Notes in Artificial Intelligence},

volume = {7958},

booktitle = {Proceedings of the 12th European Conference on Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty},

author = {Antonucci, A. and de Campos, C.P. and Huber, D. and Zaffalon, M.},

pages = {13--25},

year = {2013},

doi = {10.1007/978-3-642-39091-3_2},

url = {}

}

(2013). An ensemble of Bayesian networks for multilabel classification. In *Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13)*, pp. 1220–1225.

@INPROCEEDINGS{antonucci2013d,

title = {An ensemble of {B}ayesian networks for multilabel classification},

booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence ({IJCAI}-13)},

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Downloadtitle = {An ensemble of {B}ayesian networks for multilabel classification},

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pages = {1220--1225},

year = {2013},

doi = {},

url = {}

}

(2013). CREDO: a military decision-support system based on credal networks. In *Proceedings of the 16th Conference on Information Fusion (FUSION 2013)*, pp. 1–8.

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title = {{CREDO}: a military decision-support system based on credal networks},

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Downloadtitle = {{CREDO}: a military decision-support system based on credal networks},

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pages = {1--8},

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(2013). Temporal data classification by imprecise dynamical models. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds), *ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 13–22.

@INPROCEEDINGS{antonucci2013b,

title = {Temporal data classification by imprecise dynamical models},

editor = {Cozman, F.G. and Denoeux, T. and Destercke, S. and Seidenfeld, T.},

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booktitle = {{ISIPTA} '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications},

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

pages = {13--22},

year = {2013},

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Downloadtitle = {Temporal data classification by imprecise dynamical models},

editor = {Cozman, F.G. and Denoeux, T. and Destercke, S. and Seidenfeld, T.},

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booktitle = {{ISIPTA} '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications},

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

pages = {13--22},

year = {2013},

doi = {},

url = {http://www.sipta.org/isipta13/proceedings/papers/s002.pdf}

}

(2013). Nonlinear nonparametric mixed-effects models for unsupervised classification. *Computational Statistics* **28**(4), pp. 1549–1570.

@ARTICLE{azzimonti2013a,

title = {Nonlinear nonparametric mixed-effects models for unsupervised classification},

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volume = {28},

author = {Azzimonti, L. and Ieva, F. and Paganoni, A.M.},

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}

Downloadtitle = {Nonlinear nonparametric mixed-effects models for unsupervised classification},

journal = {Computational Statistics},

volume = {28},

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number = {4},

pages = {1549--1570},

year = {2013},

doi = {10.1007/s00180-012-0366-5},

url = {}

}

(2013). Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks. In *Proceedings of the 27th AAAI Conference on Advances in Artificial Intelligence (AAAI)*, pp. 217–223.

@INPROCEEDINGS{decampos2013a,

title = {Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks},

booktitle = {Proceedings of the 27th {AAAI} Conference on Advances in Artificial Intelligence ({AAAI})},

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}

(2013). Discovering subgroups of patients from DNA copy number data using NMF on compacted matrices. *PLoS ONE* **8**(11), e79720.

@ARTICLE{decampos2013d,

title = {Discovering subgroups of patients from {DNA} copy number data using {NMF} on compacted matrices},

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number = {11},

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year = {2013},

doi = {10.1371/journal.pone.0079720},

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}

(2013). A Bayesian network model for predicting pregnancy after in vitro fertilization. *Computers in Biology and Medicine* **43**(11), pp. 1783–1792.

@ARTICLE{corani2013c,

title = {A {B}ayesian network model for predicting pregnancy after in vitro fertilization},

journal = {Computers in Biology and Medicine},

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year = {2013},

doi = {10.1016/j.compbiomed.2013.07.035},

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}

(2013). Credal model averaging of logistic regression for modeling the distribution of marmot burrows. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds),, pp. 233–243.

@INPROCEEDINGS{corani2013a,

title = {Credal model averaging of logistic regression for modeling the distribution of marmot burrows},

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(2013). Objective way to support embryo transfer: a probabilistic decision. *Human Reproduction* **28**(5), pp. 1210–1220.

@ARTICLE{corani2013d,

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journal = {Human Reproduction},

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}

(2013). Prognostic impact of monocyte count at presentation in mantle cell lymphoma. *British Journal of Haematology* **162**(4), pp. 465–473.

@ARTICLE{decampos2013c,

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volume = {162},

author = {von Hohenstaufen, K.A. and Conconi, A. and de Campos, C.P. and Franceschetti, S. and Bertoni, F. and Margiotta Casaluci, G. and Stathis, A. and Ghielmini, M. and Stussi, G. and Cavalli, F. and Gaidano, G. and Zucca, E.},

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year = {2013},

doi = {10.1111/bjh.12409},

url = {http://onlinelibrary.wiley.com/doi/10.1111/bjh.12409/pdf}

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(2013). New prior near-ignorance models on the simplex. In Cozman, F.G., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds), *ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, Compiegne (FR), pp. 1–9.

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title = {New prior near-ignorance models on the simplex},

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(2013). On the complexity of strong and epistemic credal networks. In *Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence*, AUAI Press, pp. 391–400.

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}

(2013). On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables. *Artificial Intelligence* **205**, pp. 30–38.

@ARTICLE{maua2013b,

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pages = {30--38},

year = {2013},

doi = {10.1016/j.artint.2013.10.002},

url = {}

}

(2013). Conglomerable coherent lower previsions. In Kruse, R., Berthold, M. R., Moewes, C., Gil, M. A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Synergies of Soft Computing and Statistics for Intelligent Data Analysis*, Advances in Intelligent and Soft Computing **190**, Springer Berlin Heidelberg, pp. 419–427.

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title = {Conglomerable coherent lower previsions},

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pages = {419--427},

year = {2013},

doi = {10.1007/978-3-642-33042-1_45},

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}

(2013). Conglomerable coherence. *International Journal of Approximate Reasoning* **54**(9), pp. 1322–1350.

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title = {Conglomerable coherence},

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(2013). Computing the conglomerable natural extension. In Cozman, F., Denoeux, T., Destercke, S., Seidenfeld, T. (Eds), *ISIPTA '13: Proceedings of the Eighth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 255–264.

@INPROCEEDINGS{zaffalon2013c,

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(2013). Probability and time. *Artificial Intelligence* **198**, pp. 1–51.

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top## 2012

## An interval-valued dissimilarity measure for belief functions based on credal semantics

## Likelihood-based robust classification with Bayesian networks

## Active learning by the naive credal classifier

(2012). An interval-valued dissimilarity measure for belief functions based on credal semantics. In Denoeux, T., Masson, M.H. (Eds), *Belief Functions: Theory and Applications*, Advances in Intelligent and Soft Computing **164**, Springer Berlin / Heidelberg, pp. 37–44.

@INPROCEEDINGS{antonucci2012a,

title = {An interval-valued dissimilarity measure for belief functions based on credal semantics},

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}

(2012). Likelihood-based robust classification with Bayesian networks. In *Communications in Computer and Information Science*, Advances in Computational Intelligence **299**(5), Springer Berlin / Heidelberg, pp. 491–500.

@INPROCEEDINGS{antonucci2012b,

title = {Likelihood-based robust classification with {B}ayesian networks},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Computational Intelligence},

volume = {299},

booktitle = {Communications in Computer and Information Science},

author = {Antonucci, A. and Cattaneo, M.E.V.G. and Corani, G.},

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}

Downloadtitle = {Likelihood-based robust classification with {B}ayesian networks},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Computational Intelligence},

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author = {Antonucci, A. and Cattaneo, M.E.V.G. and Corani, G.},

number = {5},

pages = {491--500},

year = {2012},

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}

(2012). Active learning by the naive credal classifier. In Cano, A., Gomez-Olmedo, M., Nielsen, T. (Eds), *Proc. of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012)*, pp. 3–10.

@INPROCEEDINGS{antonucci2012c,

title = {Active learning by the naive credal classifier},

editor = {Cano, A. and Gomez-Olmedo, M. and Nielsen, T.},

booktitle = {Proc. {o}f the 6th European Workshop on Probabilistic Graphical Models ({PGM} 2012)},

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Downloadtitle = {Active learning by the naive credal classifier},

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