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## 2021

## Relation clustering in narrative knowledge graphs

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

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

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

## Probabilistic models with deep neural networks

(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)*.

@INPROCEEDINGS{vani2021a,

title = {Relation clustering in narrative knowledge graphs},

booktitle = {{AI4Narratives} Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence ({IJCAI}-{PRICAI} 20)},

author = {Mellace, S., Kanjirangat, V. and Antonucci, A.},

year = {2021},

url = {https://arxiv.org/abs/2011.13647}

}

Downloadtitle = {Relation clustering in narrative knowledge graphs},

booktitle = {{AI4Narratives} Workshop at 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence ({IJCAI}-{PRICAI} 20)},

author = {Mellace, S., Kanjirangat, V. and Antonucci, A.},

year = {2021},

url = {https://arxiv.org/abs/2011.13647}

}

(2021). Continuous-time system identification with neural networks: model structures and fitting criteria. *European Journal of Control*.

@ARTICLE{forgione2021b,

title = {Continuous-time system identification with neural networks: model structures and fitting criteria},

journal = {European Journal of Control},

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

year = {2021}

}

Downloadtitle = {Continuous-time system identification with neural networks: model structures and fitting criteria},

journal = {European Journal of Control},

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

year = {2021}

}

(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 = {https://doi.org/10.1007/s10994-020-05935-y}

}

Downloadtitle = {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 = {https://doi.org/10.1007/s10994-020-05935-y}

}

(2021). dynoNet: a neural network architecture for learning dynamical systems. *International Journal of Adaptive Control and Signal Processing*.

@ARTICLE{forgione2021a,

title = {{dynoNet}: a neural network architecture for learning dynamical systems},

journal = {International Journal of Adaptive Control and Signal Processing},

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

year = {2021},

url = {https://arxiv.org/abs/2006.02250}

}

Downloadtitle = {{dynoNet}: a neural network architecture for learning dynamical systems},

journal = {International Journal of Adaptive Control and Signal Processing},

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

year = {2021},

url = {https://arxiv.org/abs/2006.02250}

}

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

@ARTICLE{cabanas2021a,

title = {Probabilistic models with deep neural networks},

journal = {Entropy},

volume = {23},

author = {Masegosa, A.R. and Caba\~nas, R. and Langseth, H. and Nielsen, T.D. and Salmer\'on, A.},

number = {1},

year = {2021},

doi = {10.3390/e23010117},

url = {https://www.mdpi.com/1099-4300/23/1/117}

}

Downloadtitle = {Probabilistic models with deep neural networks},

journal = {Entropy},

volume = {23},

author = {Masegosa, A.R. and Caba\~nas, R. and Langseth, H. and Nielsen, T.D. and Salmer\'on, A.},

number = {1},

year = {2021},

doi = {10.3390/e23010117},

url = {https://www.mdpi.com/1099-4300/23/1/117}

}

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

## 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

## 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

## 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

## Recursive estimation for sparse gaussian process regression

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

## 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.

@ARTICLE{Linda2020b,

title = {A research agenda for hybrid intelligence: {A}ugmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence},

journal = {{IEEE} Computer},

volume = {53},

author = {Akata, Z. and Ballier, D. and de Rijke, M. and Dignum, F. and Dignum, V. and Eiben, G. and Fokkens, A. and Grossi, D. and Hindriks, K. and Hoos, H. and Hung, H. and Jonker, C. and Monz, C. and Neerincx, M. and Oliehoek, F. and Prakken, H. and Schlobach, S. and van der Gaag, L.C. and van Harmelen, F. and van Hoof, H. and van Riemsdijk, B. and van Wynsberghe, A. and Verbrugge, R. and Berheij, B. and Vossen, P. and Welling, M.},

pages = {18-28},

year = {2020},

doi = {10.1109/MC.2020.2996587}

}

Downloadtitle = {A research agenda for hybrid intelligence: {A}ugmenting human intellect with collaborative, adaptive, responsible and explainable artificial intelligence},

journal = {{IEEE} Computer},

volume = {53},

author = {Akata, Z. and Ballier, D. and de Rijke, M. and Dignum, F. and Dignum, V. and Eiben, G. and Fokkens, A. and Grossi, D. and Hindriks, K. and Hoos, H. and Hung, H. and Jonker, C. and Monz, C. and Neerincx, M. and Oliehoek, F. and Prakken, H. and Schlobach, S. and van der Gaag, L.C. and van Harmelen, F. and van Hoof, H. and van Riemsdijk, B. and van Wynsberghe, A. and Verbrugge, R. and Berheij, B. and Vossen, P. and Welling, M.},

pages = {18-28},

year = {2020},

doi = {10.1109/MC.2020.2996587}

}

(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.

@INPROCEEDINGS{antonucci2020a,

title = {Approximate {MMAP} by marginal search},

editor = {Brawner, K.W. and Bart\'ak, R. and Bell, E.},

publisher = {AAAI Press},

address = {North Miami Beach, Florida, USA},

booktitle = {Proceedings of the Thirty-{t}hird International Florida Artificial Intelligence Research Society Conference ({FLAIRS}-33)},

author = {Antonucci, A. and Tiotto, T.},

pages = {181--184},

year = {2020},

url = {https://www.aaai.org/Library/FLAIRS/flairs20contents.php}

}

Downloadtitle = {Approximate {MMAP} by marginal search},

editor = {Brawner, K.W. and Bart\'ak, R. and Bell, E.},

publisher = {AAAI Press},

address = {North Miami Beach, Florida, USA},

booktitle = {Proceedings of the Thirty-{t}hird International Florida Artificial Intelligence Research Society Conference ({FLAIRS}-33)},

author = {Antonucci, A. and Tiotto, T.},

pages = {181--184},

year = {2020},

url = {https://www.aaai.org/Library/FLAIRS/flairs20contents.php}

}

(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)*, JMLR.org.

@INPROCEEDINGS{azzimonti2020a,

title = {Structure learning from related data sets with a hierarchical {B}ayesian score},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

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

year = {2020},

url = {https://pgm2020.cs.aau.dk/}

}

Downloadtitle = {Structure learning from related data sets with a hierarchical {B}ayesian score},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

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

year = {2020},

url = {https://pgm2020.cs.aau.dk/}

}

(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.

@INPROCEEDINGS{azzimontid2020b,

title = {Active vs transfer learning approaches for qot estimation with small training datasets},

publisher = {Optical Society of America},

booktitle = {Optical Fiber Communication Conference ({OFC} 2020)},

author = {Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.},

pages = {M4E.1},

year = {2020},

doi = {10.1364/OFC.2020.M4E.1}

}

Downloadtitle = {Active vs transfer learning approaches for qot estimation with small training datasets},

publisher = {Optical Society of America},

booktitle = {Optical Fiber Communication Conference ({OFC} 2020)},

author = {Azzimonti, D. and Rottondi, C. and Giusti, A. and Tornatore, M. and Bianco, A.},

pages = {M4E.1},

year = {2020},

doi = {10.1364/OFC.2020.M4E.1}

}

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

@ARTICLE{azzimontid2020a,

title = {Reducing probes for quality of transmission estimation in optical networks with active learning},

journal = {J. Opt. Commun. Netw.},

publisher = {OSA},

volume = {12},

author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},

number = {1},

pages = {A38--A48},

year = {2020},

doi = {10.1364/JOCN.12.000A38}

}

Downloadtitle = {Reducing probes for quality of transmission estimation in optical networks with active learning},

journal = {J. Opt. Commun. Netw.},

publisher = {OSA},

volume = {12},

author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},

number = {1},

pages = {A38--A48},

year = {2020},

doi = {10.1364/JOCN.12.000A38}

}

(2020). Skew gaussian processes for classification. *Machine Learning* **109**.

@ARTICLE{azzimontid2020c,

title = {Skew gaussian processes for classification},

journal = {Machine Learning},

volume = {109},

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

year = {2020},

doi = {10.1007/s10994-020-05906-3}

}

Downloadtitle = {Skew gaussian processes for classification},

journal = {Machine Learning},

volume = {109},

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

year = {2020},

doi = {10.1007/s10994-020-05906-3}

}

(2020). Credici: a java library for causal inference by credal networks. In *Proceedings of the 10th International Conference on Probabilistic Graphical Models*, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.

@INPROCEEDINGS{cabanas2020a,

title = {Credici: a java library for causal inference by credal networks},

publisher = {PMLR},

address = {Aalborg, Denmark},

series = {Proceedings of Machine Learning Research},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},

author = {Caba\~nas, R. and Antonucci, A. and Huber, D. and Zaffalon, M.},

year = {2020}

}

Downloadtitle = {Credici: a java library for causal inference by credal networks},

publisher = {PMLR},

address = {Aalborg, Denmark},

series = {Proceedings of Machine Learning Research},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},

author = {Caba\~nas, R. and Antonucci, A. and Huber, D. and Zaffalon, M.},

year = {2020}

}

(2020). Probabilistic graphical models with neural networks in inferpy. In *Proceedings of the 10th International Conference on Probabilistic Graphical Models*, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.

@INPROCEEDINGS{cabanas2020b,

title = {Probabilistic graphical models with neural networks in inferpy},

publisher = {PMLR},

address = {Aalborg, Denmark},

series = {Proceedings of Machine Learning Research},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},

author = {Caba\~nas, R. and C\'ozar, J. and Salmer\'on, A. and Masegosa, A.R.},

year = {2020}

}

Downloadtitle = {Probabilistic graphical models with neural networks in inferpy},

publisher = {PMLR},

address = {Aalborg, Denmark},

series = {Proceedings of Machine Learning Research},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models},

author = {Caba\~nas, R. and C\'ozar, J. and Salmer\'on, A. and Masegosa, A.R.},

year = {2020}

}

(2020). Asynchronous optimization over graphs: linear convergence under error bound conditions. *IEEE Transactions on Automatic Control*.

@ARTICLE{cannelli2020a,

title = {Asynchronous optimization over graphs: linear convergence under error bound conditions},

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

author = {Cannelli, L. and Facchinei, F. and Scutari, G. and Kungurtsev, V.},

year = {2020},

doi = {10.1109/TAC.2020.3033490}

}

Downloadtitle = {Asynchronous optimization over graphs: linear convergence under error bound conditions},

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

author = {Cannelli, L. and Facchinei, F. and Scutari, G. and Kungurtsev, V.},

year = {2020},

doi = {10.1109/TAC.2020.3033490}

}

(2020). Social pooling of beliefs and values with desirability. *Proceedings of the 33rd International Flairs Conference (FLAIRS-33)*.

@ARTICLE{casanova2020a,

title = {Social pooling of beliefs and values with desirability},

journal = {Proceedings of the 33rd International Flairs Conference ({FLAIRS}-33)},

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

year = {2020},

url = {https://www.flairs-33.info/}

}

Downloadtitle = {Social pooling of beliefs and values with desirability},

journal = {Proceedings of the 33rd International Flairs Conference ({FLAIRS}-33)},

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

year = {2020},

url = {https://www.flairs-33.info/}

}

(2020). Detecting correlation between extreme probability events. *International Journal of General Systems* **49**(1), pp. 64–87.

@ARTICLE{Linda2019e,

title = {Detecting correlation between extreme probability events},

journal = {International Journal of General Systems },

volume = {49},

author = {Coletti, G. and van der Gaag, L.C. and Petturiti, D. and Vantaggi, B.},

number = {1},

pages = {64--87},

year = {2020},

doi = {10.1080/03081079.2019.1692005}

}

Downloadtitle = {Detecting correlation between extreme probability events},

journal = {International Journal of General Systems },

volume = {49},

author = {Coletti, G. and van der Gaag, L.C. and Petturiti, D. and Vantaggi, B.},

number = {1},

pages = {64--87},

year = {2020},

doi = {10.1080/03081079.2019.1692005}

}

(2020). Probabilistic reconciliation of hierarchical forecast via Bayes’ rule. In *Proceedings ECML - PKDD 2020*.

@INPROCEEDINGS{corani2020a,

title = {Probabilistic reconciliation of hierarchical forecast via {B}ayes’ rule},

booktitle = {Proceedings {ECML} - {PKDD} 2020},

author = {Corani, G. and Azzimonti, D. and Augusto, J.P.S.C. and Zaffalon, M.},

year = {2020}

}

Downloadtitle = {Probabilistic reconciliation of hierarchical forecast via {B}ayes’ rule},

booktitle = {Proceedings {ECML} - {PKDD} 2020},

author = {Corani, G. and Azzimonti, D. and Augusto, J.P.S.C. and Zaffalon, M.},

year = {2020}

}

(2020). Inferpy: probabilistic modeling with deep neural networks made easy. *Neurocomputing* **415**, pp. 408–410.

@ARTICLE{cabanas2020c,

title = {Inferpy: probabilistic modeling with deep neural networks made easy},

journal = {Neurocomputing},

volume = {415},

author = {C\'ozar, J. and Caba\~nas, R. and Salmer\'on, A. and Masegosa, A.R.},

pages = {408--410},

year = {2020},

doi = {https://doi.org/10.1016/j.neucom.2020.07.117},

url = {http://www.sciencedirect.com/science/article/pii/S092523122031328X}

}

Downloadtitle = {Inferpy: probabilistic modeling with deep neural networks made easy},

journal = {Neurocomputing},

volume = {415},

author = {C\'ozar, J. and Caba\~nas, R. and Salmer\'on, A. and Masegosa, A.R.},

pages = {408--410},

year = {2020},

doi = {https://doi.org/10.1016/j.neucom.2020.07.117},

url = {http://www.sciencedirect.com/science/article/pii/S092523122031328X}

}

(2020). Model structures and fitting criteria for system identification with neural networks. In *Proceedings of the 14th IEEE International Conference Application of Information and Communication Technologies (AICT 20)*.

@INPROCEEDINGS{forgione2020b,

title = {Model structures and fitting criteria for system identification with neural networks},

booktitle = {Proceedings of the 14th {IEEE} International Conference Application of Information and Communication Technologies ({AICT} 20)},

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

year = {2020},

url = {https://arxiv.org/pdf/1911.13034.pdf}

}

Downloadtitle = {Model structures and fitting criteria for system identification with neural networks},

booktitle = {Proceedings of the 14th {IEEE} International Conference Application of Information and Communication Technologies ({AICT} 20)},

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

year = {2020},

url = {https://arxiv.org/pdf/1911.13034.pdf}

}

(2020). Efficient Calibration of Embedded MPC. In *Proceedings of the 21st IFAC World Congress (IFAC 20)*.

@INPROCEEDINGS{forgione2020a,

title = {Efficient {C}alibration of {E}mbedded {MPC}},

booktitle = {Proceedings of the 21st {IFAC} World Congress ({IFAC} 20)},

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

year = {2020},

url = {https://arxiv.org/pdf/1911.13021.pdf}

}

Downloadtitle = {Efficient {C}alibration of {E}mbedded {MPC}},

booktitle = {Proceedings of the 21st {IFAC} World Congress ({IFAC} 20)},

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

year = {2020},

url = {https://arxiv.org/pdf/1911.13021.pdf}

}

(2020). Poset representations for sets of elementary triplets. In *Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020)*, JMLR.org.

@INPROCEEDINGS{Linda2020c,

title = {Poset representations for sets of elementary triplets},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

author = {van der Gaag, L.C. and Bolt, J.H.},

year = {2020},

url = {https://pgm2020.cs.aau.dk/index.php/accepted-papers/}

}

Downloadtitle = {Poset representations for sets of elementary triplets},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

author = {van der Gaag, L.C. and Bolt, J.H.},

year = {2020},

url = {https://pgm2020.cs.aau.dk/index.php/accepted-papers/}

}

(2020). Building causal interaction models by recursive unfolding. In *Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020)*, JMLR.org.

@INPROCEEDINGS{Linda2020a,

title = {Building causal interaction models by recursive unfolding},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

author = {van der Gaag, L.C. and Renooij, S. and Facchini, A.},

year = {2020},

url = {https://pgm2020.cs.aau.dk/index.php/accepted-papers/}

}

Downloadtitle = {Building causal interaction models by recursive unfolding},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

author = {van der Gaag, L.C. and Renooij, S. and Facchini, A.},

year = {2020},

url = {https://pgm2020.cs.aau.dk/index.php/accepted-papers/}

}

(2020). Learning probabilistic sentential decision diagrams by sampling. In *Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning (KMILE 2020)*, SBC, Porto Alegre, RS, Brasil, pp. 129–136.

@INPROCEEDINGS{antonucci2020c,

title = {Learning probabilistic sentential decision diagrams by sampling},

publisher = {SBC},

address = {Porto Alegre, RS, Brasil},

booktitle = {Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning ({KMILE} 2020)},

author = {Geh, R. and Mau\'a, D.D. and Antonucci, A.},

pages = {129--136},

year = {2020},

doi = {10.5753/kdmile.2020.11968}

}

Downloadtitle = {Learning probabilistic sentential decision diagrams by sampling},

publisher = {SBC},

address = {Porto Alegre, RS, Brasil},

booktitle = {Proceedings of the Eight Symposium on Knowledge Discovery, Mining and Learning ({KMILE} 2020)},

author = {Geh, R. and Mau\'a, D.D. and Antonucci, A.},

pages = {129--136},

year = {2020},

doi = {10.5753/kdmile.2020.11968}

}

(2020). CREMA: a Java library for credal network inference. In Jaeger, M., Nielsen, T.D. (Eds), *Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020)*, Proceedings of Machine Learning Research, PMLR, Aalborg, Denmark.

@INPROCEEDINGS{huber2020a,

title = {{CREMA}: a {J}ava library for credal network inference},

editor = {Jaeger, M. and Nielsen, T.D.},

publisher = {PMLR},

address = {Aalborg, Denmark},

series = {Proceedings of Machine Learning Research},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

author = {Huber, D. and Caba\~nas, R. and Antonucci, A. and Zaffalon, M.},

year = {2020},

url = {https://pgm2020.cs.aau.dk}

}

Downloadtitle = {{CREMA}: a {J}ava library for credal network inference},

editor = {Jaeger, M. and Nielsen, T.D.},

publisher = {PMLR},

address = {Aalborg, Denmark},

series = {Proceedings of Machine Learning Research},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

author = {Huber, D. and Caba\~nas, R. and Antonucci, A. and Zaffalon, M.},

year = {2020},

url = {https://pgm2020.cs.aau.dk}

}

(2020). Temporal embeddings and transformer models for narrative text understanding. In *Third International Workshop on Narrative Extraction from Texts (Text2Story 20), 42nd European Conference on Information Retrieval (ECIR 20)*, ceur.

@INPROCEEDINGS{vani2020b,

title = {Temporal embeddings and transformer models for narrative text understanding},

publisher = {ceur},

booktitle = {Third International Workshop on Narrative Extraction {f}rom Texts ({Text2Story} 20), 42nd European Conference on Information Retrieval ({ECIR} 20)},

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author = {Kanjirangat, V. and Mellace, S. and Antonucci, A.},

year = {2020},

url = {http://ceur-ws.org/Vol-2593/paper9.pdf}

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title = {Impact on place of death in cancer patients: a causal exploration in southern switzerland},

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pages = {160},

year = {2020},

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year = {2020},

doi = {10.1016/j.automatica.2020.108914},

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title = {A {B}ayesian approach to conversational recommendation systems},

journal = {{AAAI} 2020 Workshop on Interactive and Conversational Recommendation Systems ({WICRS}-20)},

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

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title = {{6D} virtual sensor for wrench estimation in robotized interaction tasks exploiting extended {K}alman filter},

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

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

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

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

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url = {https://dl.acm.org/doi/pdf/10.1145/3336191.3371815}

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@INPROCEEDINGS{zaffalon2020b,

title = {Structural causal models are (solvable by) credal networks},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

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

year = {2020},

url = {https://pgm2020.cs.aau.dk/}

}

Downloadtitle = {Structural causal models are (solvable by) credal networks},

publisher = {JMLR.org},

booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models ({PGM} 2020)},

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

year = {2020},

url = {https://pgm2020.cs.aau.dk/}

}

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

## Desirability foundations of robust rational decision making

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

@INPROCEEDINGS{supsi2019c,

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

publisher = {AAAI Press},

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

author = {Antonucci, A.},

year = {2019},

url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18228/17346}

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

publisher = {AAAI Press},

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

author = {Antonucci, A.},

year = {2019},

url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18228/17346}

}

(2019). Credal sentential decision diagrams. In *Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19)*.

@INPROCEEDINGS{supsi2019b,

title = {Credal sentential decision diagrams},

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

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

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url = {http://www.isipta2019.ugent.be/contributions/antonucci19.pdf}

}

Downloadtitle = {Credal sentential decision diagrams},

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

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

year = {2019},

url = {http://www.isipta2019.ugent.be/contributions/antonucci19.pdf}

}

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

@ARTICLE{azzimonti2018a,

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

journal = {Journal of Multivariate Analysis},

volume = {170},

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

pages = {275-295},

year = {2019},

doi = {10.1016/j.jmva.2018.09.006}

}

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

journal = {Journal of Multivariate Analysis},

volume = {170},

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

pages = {275-295},

year = {2019},

doi = {10.1016/j.jmva.2018.09.006}

}

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

@ARTICLE{azzimonti2019a,

title = {Hierarchical estimation of parameters in {B}ayesian networks},

journal = {Computational Statistics and Data Analysis},

volume = {137},

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

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year = {2019},

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}

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journal = {Computational Statistics and Data Analysis},

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pages = {67--91},

year = {2019},

doi = {10.1016/j.csda.2019.02.004}

}

(2019). Adaptive design of experiments for conservative estimation of excursion sets. *Technometrics*, pp. 1–30.

@ARTICLE{azzimontid2019c,

title = {Adaptive design of experiments for conservative estimation of excursion sets},

journal = {Technometrics},

publisher = {Taylor & Francis},

author = {Azzimonti, D. and Ginsbourger, D. and Chevalier, C. and Bect, J. and Richet, Y.},

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year = {2019},

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}

Downloadtitle = {Adaptive design of experiments for conservative estimation of excursion sets},

journal = {Technometrics},

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pages = {1--30},

year = {2019},

doi = {10.1080/00401706.2019.1693427}

}

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

@ARTICLE{azzimontid2019a,

title = {Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding},

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Downloadtitle = {Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding},

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number = {4},

pages = {474--493},

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doi = {10.1080/00401706.2018.1562987}

}

(2019). Using active learning to decrease probes for QoT estimation in optical networks. In , Optical Society of America, Th1H.1.

@INPROCEEDINGS{azzimontid2019b,

title = {Using active learning to decrease probes for {QoT} estimation in optical networks},

journal = {Optical Fiber Communication Conference ({OFC}) 2019},

publisher = {Optical Society of America},

author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},

pages = {Th1H.1},

year = {2019},

doi = {10.1364/OFC.2019.Th1H.1}

}

Downloadtitle = {Using active learning to decrease probes for {QoT} estimation in optical networks},

journal = {Optical Fiber Communication Conference ({OFC}) 2019},

publisher = {Optical Society of America},

author = {Azzimonti, D. and Rottondi, C. and Tornatore, M.},

pages = {Th1H.1},

year = {2019},

doi = {10.1364/OFC.2019.Th1H.1}

}

(2019). Bernstein's socks, polynomial-time provable coherence and entanglement. In De Bock, J., de Campos, C., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), *ISIPTA '19: Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications*, PMLR **103**, JMLR.org, pp. 23-31.

@INPROCEEDINGS{zaffalon2019b,

title = {Bernstein's socks, polynomial-time provable coherence and entanglement},

editor = {De Bock, J. and de Campos, C. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

volume = {103},

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

author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},

pages = {23-31},

year = {2019},

url = {http://proceedings.mlr.press/v103/benavoli19a/benavoli19a.pdf}

}

Downloadtitle = {Bernstein's socks, polynomial-time provable coherence and entanglement},

editor = {De Bock, J. and de Campos, C. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

volume = {103},

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

author = {Benavoli, A. and Facchini, A. and Zaffalon, M.},

pages = {23-31},

year = {2019},

url = {http://proceedings.mlr.press/v103/benavoli19a/benavoli19a.pdf}

}

(2019). On minimum elementary-triplet bases for independence relations. In De Bock, J., de Campos, C.P., de Cooman, G., Quaeghebeur, E., Wheeler, G. (Eds), *Proceedings of the Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA '19)*, PMLR **103**, JMLR.org, pp. 32–37.

@INPROCEEDINGS{Linda2019c,

title = {On minimum elementary-triplet bases for independence relations},

editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

volume = {103},

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

author = {Bolt, J.H. and van der Gaag, L.C.},

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year = {2019},

url = {http://www.sipta.org/isipta19/contributions/bolt19.pdf}

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Downloadtitle = {On minimum elementary-triplet bases for independence relations},

editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

volume = {103},

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

author = {Bolt, J.H. and van der Gaag, L.C.},

pages = {32--37},

year = {2019},

url = {http://www.sipta.org/isipta19/contributions/bolt19.pdf}

}

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

@ARTICLE{piga2019b,

title = {Online end-use energy disaggregation via jump linear models},

journal = {Control Engineering Practice},

volume = {89},

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

pages = {30--42},

year = {2019},

doi = {10.1016/j.conengprac.2019.05.011}

}

Downloadtitle = {Online end-use energy disaggregation via jump linear models},

journal = {Control Engineering Practice},

volume = {89},

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

pages = {30--42},

year = {2019},

doi = {10.1016/j.conengprac.2019.05.011}

}

(2019). From location tracking to personalized eco-feedback: a framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. *Travel Behaviour and Society* **14**, pp. 43-56.

@ARTICLE{mangili2019a,

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

journal = {Travel Behaviour and Society},

volume = {14},

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

pages = {43-56},

year = {2019},

doi = {10.1016/j.tbs.2018.09.005}

}

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

journal = {Travel Behaviour and Society},

volume = {14},

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

pages = {43-56},

year = {2019},

doi = {10.1016/j.tbs.2018.09.005}

}

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

@ARTICLE{piga2019d,

title = {Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics},

journal = {Engineering Fracture Mechanics},

volume = {205},

author = {Carollo, V. and Piga, D. and Borri, C. and Paggi, M.},

pages = {439--454},

year = {2019},

url = {https://www.sciencedirect.com/science/article/pii/S001379441830451X?via%3Dihub}

}

Downloadtitle = {Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics},

journal = {Engineering Fracture Mechanics},

volume = {205},

author = {Carollo, V. and Piga, D. and Borri, C. and Paggi, M.},

pages = {439--454},

year = {2019},

url = {https://www.sciencedirect.com/science/article/pii/S001379441830451X?via%3Dihub}

}

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

@ARTICLE{mangili2019b,

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

journal = {Sustainability},

editor = {mdpi},

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author = {Cellina, F. and Bucher, D. and Mangili, F. and Sim\~ao, J.V. and Rudel, R. and Raubal, M.},

number = {9},

pages = {2674},

year = {2019},

doi = {https://doi.org/10.3390/su11092674}

}

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journal = {Sustainability},

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number = {9},

pages = {2674},

year = {2019},

doi = {https://doi.org/10.3390/su11092674}

}

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

@ARTICLE{rinaldi2019c,

title = {Improving {spaCy} dependency annotation and {PoS} tagging web service using independent {NER} services},

journal = {Genomics Inform},

volume = {17},

author = {Colic, N. and Rinaldi, F.},

number = {2},

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year = {2019},

doi = {10.5808/GI.2019.17.2.e21}

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Downloadtitle = {Improving {spaCy} dependency annotation and {PoS} tagging web service using independent {NER} services},

journal = {Genomics Inform},

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number = {2},

pages = {e21},

year = {2019},

doi = {10.5808/GI.2019.17.2.e21}

}

(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.

@INPROCEEDINGS{Linda2910d,

title = {An experimental study of prior dependence in {B}ayesian network structure learning},

editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

publisher = {JMLR.org},

series = {PMLR},

volume = {103},

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

author = {Correia, A.H.C. and de Campos, C.P. and van der Gaag, L.C.},

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Downloadtitle = {An experimental study of prior dependence in {B}ayesian network structure learning},

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series = {PMLR},

volume = {103},

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

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pages = {78--81},

year = {2019},

url = {http://www.sipta.org/isipta19/contributions/correia19.pdf}

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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.

@INPROCEEDINGS{rinaldi2019g,

title = {Approaching {SMM4H} with merged models and multi-task learning},

publisher = {Association for Computational Linguistics},

booktitle = {Proceedings of the Fourth Social Media Mining for Health Applications (\#smm4h) Workshop & Shared Task},

author = {Ellendorff, T. and Furrer, L. and Colic, N. and Aepli, N. and Rinaldi, F.},

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Downloadtitle = {Approaching {SMM4H} with merged models and multi-task learning},

publisher = {Association for Computational Linguistics},

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pages = {58--61},

year = {2019},

doi = {10.18653/v1/W19-3208},

url = {https://www.aclweb.org/anthology/W19-3208}

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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.

@INPROCEEDINGS{rinaldi2019h,

title = {{UZH@CRAFT}-{ST}: a sequence-labeling approach to concept recognition},

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Downloadtitle = {{UZH@CRAFT}-{ST}: a sequence-labeling approach to concept recognition},

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

@ARTICLE{rinaldi2019d,

title = {Oger++: hybrid multi-type entity recognition},

journal = {Journal of Cheminformatics},

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author = {Furrer, L. and Jancso, A. and Colic, N. and Rinaldi, F.},

number = {1},

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journal = {Journal of Cheminformatics},

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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.

@ARTICLE{vani2019a,

title = {{NOVEL2GRAPH}: {V}isual summaries of narrative text enhanced by machine learning},

journal = {Proceedings of the {Text2StoryIR'19} Workshop, Cologne, Germany, 14-April-2019},

editor = {A. Jorge, R. Campos, A. Jatowt, S. Bhatia},

publisher = {ceur},

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pages = {29--37},

year = {2019},

url = {http://ceur-ws.org/Vol-2342/paper4.pdf}

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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.

@INPROCEEDINGS{supsi2019d,

title = {Semantically corroborating neural attention for biomedical question answering},

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

@ARTICLE{rinaldi2019b,

title = {Introduction to {BLAH5} special issue: recent progress on interoperability of biomedical text mining},

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Downloadtitle = {Introduction to {BLAH5} special issue: recent progress on interoperability of biomedical text mining},

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number = {2},

pages = {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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volume = {103},

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

author = {Renooij, S. and van der Gaag, L.C. and Leray, Ph.},

pages = {327--329},

year = {2019},

url = {http://www.sipta.org/isipta19/contributions/renooij19.pdf}

}

Downloadtitle = {On intercausal interactions in probabilistic relational models},

editor = {De Bock, J. and de Campos, C.P. and de Cooman, G. and Quaeghebeur, E. and Wheeler, G.},

series = {Proceedings of Machine Learning Research},

volume = {103},

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

author = {Renooij, S. and van der Gaag, L.C. and Leray, Ph.},

pages = {327--329},

year = {2019},

url = {http://www.sipta.org/isipta19/contributions/renooij19.pdf}

}

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

@INPROCEEDINGS{supsi2019a,

title = {Exploring the space of probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning},

author = {Mattei, L. and Soares, D.L. and Antonucci, A. and Mau\`a, D.D. and Facchini, A.},

year = {2019},

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Downloadtitle = {Exploring the space of probabilistic sentential decision diagrams},

booktitle = {Proceedings of the 3rd Tractable Probabilistic Modeling Workshop, 36th International Conference on Machine Learning},

author = {Mattei, L. and Soares, D.L. and Antonucci, A. and Mau\`a, D.D. and Facchini, A.},

year = {2019},

url = {https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view}

}

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

@ARTICLE{Roveda2019b,

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

journal = {{MDPI} Robotics},

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

year = {2019},

doi = {10.3390/robotics8030065}

}

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

journal = {{MDPI} Robotics},

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

year = {2019},

doi = {10.3390/robotics8030065}

}

(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},

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

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}

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

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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}

}

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

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

@ARTICLE{corani2019b,

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

journal = {Journal of Lightwave Technology},

volume = {37},

author = {Musumeci, F. and Rottondi, C.E.M. and Corani, G. and Shahkarami, S. and Cugini, F. and Tornatore, M.},

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

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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.

@INPROCEEDINGS{oita2019innGenuity,

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Downloadtitle = {Reverse engineering creativity into interpretable neural networks},

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year = {2019},

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

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(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,

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Downloadtitle = {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.

@ARTICLE{piga2019c,

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

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year = {2019},

doi = {10.1080/00207179.2018.1557348}

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

@INPROCEEDINGS{piga2019f,

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

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(2019). The hidden elegance of causal interaction models. In Ben Amor, N., Quost, B., Theobald, M (Eds), *13th International Conference on Scalable Uncertainty Management (SUM '19)*, Lecture Notes in Artificial Intelligence **11940**, Springer, pp. 38–51.

@INPROCEEDINGS{linda2019a,

title = {The hidden elegance of causal interaction models},

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series = {Lecture Notes in Artificial Intelligence},

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booktitle = {13th International Conference on Scalable Uncertainty Management ({SUM} '19)},

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pages = {38--51},

year = {2019},

doi = {10.1007/978-3-030-35514-2_4}

}

(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},

journal = {{bioRxiv}},

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

year = {2019},

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url = {https://www.biorxiv.org/content/early/2019/05/12/633255}

}

(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}},

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

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

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pages = {75},

year = {2019},

doi = {10.3389/frobt.2019.00075}

}

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

@ARTICLE{corani2019a,

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

journal = {Computers & Operations Research},

volume = {108},

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doi = {10.1016/j.cor.2019.04.009}

}

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

journal = {Computers & Operations Research},

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

pages = {112--120},

year = {2019},

doi = {10.1016/j.cor.2019.04.009}

}

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

@ARTICLE{azzimonti2019b,

title = {Efficient feature selection using shrinkage estimators},

journal = {Machine Learning},

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}

}

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},

pages = {1261--1286},

year = {2019},

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

}

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

@ARTICLE{rinaldi2019f,

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

journal = {{JMIR} Med Inform},

volume = {7},

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

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

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pages = {e12239},

year = {2019},

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}},

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volume = {26},

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

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number = {10},

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year = {2019},

doi = {10.1093/jamia/ocz028}

}

(2019). Desirability foundations of robust rational decision making. *Synthese*.

@ARTICLE{zaffalon2019a,

title = {Desirability foundations of robust rational decision making},

journal = {Synthese},

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

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doi = {10.1007/s11229-018-02010-x}

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Downloadtitle = {Desirability foundations of robust rational decision making},

journal = {Synthese},

publisher = {Springer},

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

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doi = {10.1007/s11229-018-02010-x}

}

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},

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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},

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}

}

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}

}

(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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pages = {2247--2252},

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doi = {10.1109/CDC.2018.8619819}

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

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

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pages = {2247--2252},

year = {2018},

doi = {10.1109/CDC.2018.8619819}

}

(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},

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

number = {5},

pages = {108--113},

year = {2018},

doi = {10.1016/j.ifacol.2018.06.219}

}

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}

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(2018). Jump model learning and filtering for energy end-use disaggregation. In *Proc. of the 18th IFAC Symposium on System Identification* **51**(15), pp. 275–280.

@INPROCEEDINGS{piga2018f,

title = {Jump model learning and filtering for energy end-use disaggregation},

volume = {51},

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

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

number = {15},

pages = {275--280},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.147}

}

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}

}

(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}

}

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}

}

(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}

}

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}

}

(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},

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},

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},

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year = {2018},

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

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Downloadtitle = {Imaginary kinematics},

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

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pages = {104-113},

year = {2018},

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.},

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

volume = {51},

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

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number = {15},

pages = {1092--1097},

year = {2018},

doi = {10.1016/j.ifacol.2018.09.048}

}

(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)},

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

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

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pages = {3116--3121},

year = {2018},

doi = {10.1109/CDC.2018.8619175}

}

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

@ARTICLE{piga2018c,

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

journal = {Automatica},

volume = {87},

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

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doi = {10.1016/j.automatica.2017.09.014}

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

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pages = {128--141},

year = {2018},

doi = {10.1016/j.automatica.2017.09.014}

}

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

@ARTICLE{piga2018a,

title = {Direct data-driven control of constrained systems},

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

volume = {26},

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

number = {4},

pages = {1422--1429},

year = {2018},

doi = {10.1109/TCST.2017.2702118}

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number = {4},

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year = {2018},

doi = {10.1109/TCST.2017.2702118}

}

(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.

@ARTICLE{scanagatta2018a,

title = {Efficient learning of bounded-treewidth {B}ayesian networks from complete and incomplete data sets},

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pages = {152--166},

year = {2018},

doi = {10.1016/j.ijar.2018.02.004}

}

(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},

booktitle = {2018 European Control Conference ({e}cc)},

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doi = {10.23919/ECC.2018.8550184}

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

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doi = {10.23919/ECC.2018.8550184}

}

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}

}

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}

}

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

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

pages = {739--744},

year = {2017},

doi = {10.1109/ICDM.2017.85}

}

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

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

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number = {1},

pages = {1-2},

year = {2017},

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

}

(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.},

number = {11},

pages = {1817--1837},

year = {2017},

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

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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}

}

(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.},

number = {1},

pages = {53--66},

year = {2017},

doi = {10.1111/papr.12581}

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publisher = {Wiley},

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number = {1},

pages = {53--66},

year = {2017},

doi = {10.1111/papr.12581}

}

(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},

journal = {Blood},

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

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number = {26},

pages = {3440-3451},

year = {2017},

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

}

(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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booktitle = {Symbolic and Quantitative Approaches to Reasoning {w}ith Uncertainty. {ECSQARU} 2017 },

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pages = {282--291},

year = {2017},

doi = {10.1007/978-3-319-61581-3_26}

}

(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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number = {99},

year = {2017},

doi = {10.1109/TAC.2017.2699281}

}

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

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volume = {PP},

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number = {99},

year = {2017},

doi = {10.1109/TAC.2017.2699281}

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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},

editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone},

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series = {Proceedings of Machine Learning Research},

volume = {73},

booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks ({AMBN})},

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

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pages = {45-56},

year = {2017},

url = {http://proceedings.mlr.press/v73/scanagatta17a.html}

}

(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.},

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {456},

booktitle = {Soft Methods for Data Science},

author = {Soullard, Y. and Antonucci, A. and Destercke, S.},

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Downloadtitle = {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.},

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {456},

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author = {Soullard, Y. and Antonucci, A. and Destercke, S.},

pages = {455--462},

year = {2017},

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

}

(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.

@INCOLLECTION{zaffalon2017a,

title = {Full conglomerability, continuity and marginal extension},

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

publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

volume = {456},

booktitle = {Soft Methods for Data Science},

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pages = {355-362},

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publisher = {Springer},

series = {Advances in Intelligent Systems and Computing},

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

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title = {Full conglomerability},

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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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title = {Axiomatising incomplete preferences through sets of desirable gambles},

journal = {Journal of Artificial Intelligence Research},

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volume = {60},

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

pages = {1057--1126},

year = {2017},

doi = {10.1613/jair.5230}

}

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}

}

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}

}

(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}

}

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}

}

(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}

}

Downloadtitle = {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}

}

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

@ARTICLE{deCampos2016,

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

journal = {New Generation Computing},

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

pages = {1--18},

year = {2016},

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

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

}

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

journal = {New Generation Computing},

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

pages = {1--18},

year = {2016},

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

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

}

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

@ARTICLE{decampos2015a,

title = {Learning extended tree augmented naive structures},

journal = {International Journal of Approximate Reasoning.},

volume = {68},

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

pages = {153--163},

year = {2016},

doi = {10.1016/j.ijar.2015.04.006}

}

Downloadtitle = {Learning extended tree augmented naive structures},

journal = {International Journal of Approximate Reasoning.},

volume = {68},

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

pages = {153--163},

year = {2016},

doi = {10.1016/j.ijar.2015.04.006}

}

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

@ARTICLE{corani2016a,

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

journal = {Environmental Modelling & Software},

volume = {80},

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

pages = {259--264},

year = {2016},

doi = {10.1016/j.envsoft.2016.02.030}

}

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

journal = {Environmental Modelling & Software},

volume = {80},

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

pages = {259--264},

year = {2016},

doi = {10.1016/j.envsoft.2016.02.030}

}

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

@ARTICLE{shuaiFu2015a,

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

journal = {Journal of Statistical Computation and Simulation},

author = {Fu, S.},

year = {2016},

doi = {10.1080/00949655.2015.1106541}

}

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

journal = {Journal of Statistical Computation and Simulation},

author = {Fu, S.},

year = {2016},

doi = {10.1080/00949655.2015.1106541}

}

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

@ARTICLE{azzimonti2016a,

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

journal = {Journal of Biomechanics},

editor = {Elsevier},

volume = {49},

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

number = {1},

pages = {26--38},

year = {2016},

doi = {10.1016/j.jbiomech.2015.11.009}

}

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

journal = {Journal of Biomechanics},

editor = {Elsevier},

volume = {49},

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

number = {1},

pages = {26--38},

year = {2016},

doi = {10.1016/j.jbiomech.2015.11.009}

}

(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 },

author = {Mangili, F.},

year = {2016},

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

}

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

journal = {International Journal of Approximate Reasoning },

author = {Mangili, F.},

year = {2016},

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

}

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

@INPROCEEDINGS{mangili2016a,

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

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

series = {Lecture Notes in Computer Science},

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

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

pages = {471--472},

year = {2016},

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

}

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

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

series = {Lecture Notes in Computer Science},

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

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

pages = {471--472},

year = {2016},

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

}

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

@ARTICLE{antonucci2015c,

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

journal = {Neurocomputing},

volume = {180},

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

pages = {94--107},

year = {2016},

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

}

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

journal = {Neurocomputing},

volume = {180},

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

pages = {94--107},

year = {2016},

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

}

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

@ARTICLE{zaffalon2016c,

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

journal = {International Journal of Approximate Reasoning},

volume = {78},

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

pages = {125--137},

year = {2016},

doi = {10.1016/j.ijar.2016.07.004}

}

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

journal = {International Journal of Approximate Reasoning},

volume = {78},

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

pages = {125--137},

year = {2016},

doi = {10.1016/j.ijar.2016.07.004}

}

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

@ARTICLE{zaffalon2015b,

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

journal = {Computational Statistics and Data Analysis},

volume = {93},

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

pages = {373--387},

year = {2016},

doi = {10.1016/j.csda.2014.12.008}

}

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

journal = {Computational Statistics and Data Analysis},

volume = {93},

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pages = {373--387},

year = {2016},

doi = {10.1016/j.csda.2014.12.008}

}

(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*.

@INPROCEEDINGS{scanagatta2016a,

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

journal = {{NIPS}},

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

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

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

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

}

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

journal = {{NIPS}},

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

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

year = {2016},

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

}

top## 2015

## The multilabel naive credal classifier

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

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

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

## 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.},

publisher = {SIPTA},

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

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

pages = {27--36},

year = {2015},

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

}

Downloadtitle = {The multilabel naive credal classifier},

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

publisher = {SIPTA},

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

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

pages = {27--36},

year = {2015},

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}

}

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

journal = {International Journal of Approximate Reasoning},

volume = {56},

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

number = {B},

pages = {249--263},

year = {2015},

doi = {10.1016/j.ijar.2014.07.005}

}

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

@INPROCEEDINGS{antonucci2015d,

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

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

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

year = {2015},

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

}

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

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

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

year = {2015},

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

}

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

@ARTICLE{azzimonti2015a,

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

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

volume = {110},

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

number = {511},

pages = {1057--1071},

year = {2015},

doi = {10.1080/01621459.2014.946036}

}

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

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

volume = {110},

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

number = {511},

pages = {1057--1071},

year = {2015},

doi = {10.1080/01621459.2014.946036}

}

(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.},

series = {Lecture Notes in Computer Science},

volume = {9161},

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.},

pages = {541--551},

year = {2015},

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

doi = {10.1007/978-3-319-20807-7_49}

}

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

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

series = {Lecture Notes in Computer Science},

volume = {9161},

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.},

pages = {541--551},

year = {2015},

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

doi = {10.1007/978-3-319-20807-7_49}

}

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

@INCOLLECTION{antonucci2015e,

title = {Imprecision in machine learning and {AI}},

publisher = {IEEE Computer Society},

volume = {16},

booktitle = {The {IEEE} Intelligent Informatics Bulletin},

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

number = {1},

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year = {2015},

url = {http://www.comp.hkbu.edu.hk/~cib/2015/Dec/iib_vol16no1.pdf}

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Downloadtitle = {Imprecision in machine learning and {AI}},

publisher = {IEEE Computer Society},

volume = {16},

booktitle = {The {IEEE} Intelligent Informatics Bulletin},

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number = {1},

pages = {20--23},

year = {2015},

url = {http://www.comp.hkbu.edu.hk/~cib/2015/Dec/iib_vol16no1.pdf}

}

(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},

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

number = {2},

pages = {285--304},

year = {2015},

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

}

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

journal = {Machine Learning},

volume = {100},

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

number = {2},

pages = {285--304},

year = {2015},

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

}

(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)},

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

pages = {199--202},

year = {2015},

doi = {10.1007/978-3-319-23461-8_13}

}

Downloadtitle = {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)},

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

pages = {199--202},

year = {2015},

doi = {10.1007/978-3-319-23461-8_13}

}

(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.},

journal = {International Journal of Approximate Reasoning},

volume = {56},

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

number = {B},

pages = {264--277},

year = {2015},

doi = {10.1016/j.ijar.2014.07.001}

}

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

journal = {International Journal of Approximate Reasoning},

volume = {56},

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

number = {B},

pages = {264--277},

year = {2015},

doi = {10.1016/j.ijar.2014.07.001}

}

(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},

volume = {22},

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

number = {3},

pages = {513--534},

year = {2015},

doi = {10.1007/s10651-014-0308-1}

}

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

journal = {Environmental and Ecological Statistics},

volume = {22},

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

number = {3},

pages = {513--534},

year = {2015},

doi = {10.1007/s10651-014-0308-1}

}

(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}

}

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}

}

(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,

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

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},

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

}

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

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},

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.},

number = {Part B},

pages = {278--306},

year = {2015},

doi = {10.1016/j.ijar.2014.08.005}

}

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

journal = {International Journal of Approximate Reasoning},

volume = {56},

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

number = {Part B},

pages = {278--306},

year = {2015},

doi = {10.1016/j.ijar.2014.08.005}

}

(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},

author = {Mangili, F. and Benavoli, A. and de Campos, C.P. and Zaffalon, M.},

number = {6},

pages = {1002--1019},

year = {2015},

doi = {10.1002/bimj.201500062}

}

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

journal = {Biometrical Journal},

volume = {57},

author = {Mangili, F. and Benavoli, A. and de Campos, C.P. and Zaffalon, M.},

number = {6},

pages = {1002--1019},

year = {2015},

doi = {10.1002/bimj.201500062}

}

(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},

journal = {International Journal of Approximate Reasoning},

volume = {56},

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

number = {A},

pages = {1--27},

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}

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year = {2015},

doi = {10.1016/j.ijar.2014.09.003}

}

(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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pages = {460--488},

year = {2015},

doi = {10.1016/j.jmaa.2014.12.049}

}

Downloadtitle = {Independent products in infinite spaces},

journal = {Journal of Mathematical Analysis and Applications},

volume = {425},

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number = {1},

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year = {2015},

doi = {10.1016/j.jmaa.2014.12.049}

}

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

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

journal = {{NIPS}},

editor = {Corinna Cortes and Neil D. Lawrence and Daniel D. Lee and Masashi Sugiyama and Roman Garnett},

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url = {http://papers.nips.cc/paper/5803-learning-bayesian-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.

@ARTICLE{antonucci2014e,

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pages = {25--38},

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doi = {10.1016/j.ijar.2014.10.003}

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Downloadtitle = {Approximate credal network updating by linear programming with applications to decision making},

journal = {International Journal of Approximate Reasoning},

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pages = {25--38},

year = {2014},

doi = {10.1016/j.ijar.2014.10.003}

}

(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,

title = {Probabilistic graphical models},

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

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pages = {207--229},

year = {2014},

chapter = {9},

doi = {10.1002/9781118763117.ch9}

}

(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},

editor = {Laurent, A. and Strauss, O. and Bouchon-Meunier, B. and Yager, R.R.},

publisher = {Springer},

series = {Communications in Computer and Information Science},

volume = {444},

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

author = {Antonucci, A. and Karlsson, A. and Sundgren, D.},

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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},

journal = {{SIAM/ASA} Journal on Uncertainty Quantification},

volume = {2},

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

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

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number = {1},

pages = {305-335},

year = {2014},

doi = {10.1137/130925426}

}

(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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url = {http://papers.nips.cc/paper/5472-global-sensitivity-analysis-for-map-inference-in-graphical-models.pdf}

}

(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.

@INPROCEEDINGS{decampos2014a,

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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.

@ARTICLE{corani2012f,

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

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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.

@INPROCEEDINGS{corani2014b,

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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.

@ARTICLE{cozman2013ijar,

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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},

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

year = {2014}

}

(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},

volume = {50},

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

pages = {603--637},

year = {2014},

doi = {10.1613/jair.4355}

}

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

journal = {Journal of Artifical Intelligence Research},

volume = {50},

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pages = {603--637},

year = {2014},

doi = {10.1613/jair.4355}

}

(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},

volume = {71},

author = {Polpo, A. and de Campos, C.P. and Sinha, D. and Lipsitz, S. and Lin, J.},

pages = {903--913},

year = {2014},

doi = {10.1016/j.csda.2013.07.023}

}

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

journal = {Computational Statistics and Data Analysis},

publisher = {Elsevier},

volume = {71},

author = {Polpo, A. and de Campos, C.P. and Sinha, D. and Lipsitz, S. and Lin, J.},

pages = {903--913},

year = {2014},

doi = {10.1016/j.csda.2013.07.023}

}

(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,

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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,

title = {Comments on {"Imprecise} probability models for learning multinomial distributions from data. Applications to learning credal networks" by {A}ndr\'es {R}. Masegosa and {S}eraf\'in {M}oral},

journal = {International Journal of Approximate Reasoning},

volume = {55},

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Downloadtitle = {Comments on {"Imprecise} probability models for learning multinomial distributions from data. Applications to learning credal networks" by {A}ndr\'es {R}. Masegosa and {S}eraf\'in {M}oral},

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volume = {55},

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number = {7},

pages = {1597--1600},

year = {2014},

doi = {10.1016/j.ijar.2014.05.001}

}

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,

title = {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}

}

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}

}

(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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pages = {1220--1225},

year = {2013}

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

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

author = {Antonucci, A. and Corani, G. and Mau\'a, D.D. and Gabaglio, S.},

pages = {1220--1225},

year = {2013}

}

(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.

@INPROCEEDINGS{antonucci2013c,

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

booktitle = {Proceedings of the 16th Conference on Information Fusion ({FUSION} 2013)},

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

booktitle = {Proceedings of the 16th Conference on Information Fusion ({FUSION} 2013)},

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pages = {1--8},

year = {2013}

}

(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.},

publisher = {SIPTA},

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},

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

}

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

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

publisher = {SIPTA},

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},

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},

journal = {Computational Statistics},

volume = {28},

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

number = {4},

pages = {1549--1570},

year = {2013},

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

}

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

journal = {Computational Statistics},

volume = {28},

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

number = {4},

pages = {1549--1570},

year = {2013},

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

}

(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})},

author = {de Campos, C.P. and Cozman, F.G.},

pages = {217--223},

year = {2013}

}

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

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

author = {de Campos, C.P. and Cozman, F.G.},

pages = {217--223},

year = {2013}

}

(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},

journal = {{PLoS} {ONE}},

volume = {8},

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

number = {11},

pages = {e79720},

year = {2013},

doi = {10.1371/journal.pone.0079720}

}

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

journal = {{PLoS} {ONE}},

volume = {8},

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

number = {11},

pages = {e79720},

year = {2013},

doi = {10.1371/journal.pone.0079720}

}

(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},

volume = {43},

author = {Corani, G. and Magli, M. and Giusti, A. and Gianaroli, L. and Gambardella, L.},

number = {11},

pages = {1783--1792},

year = {2013},

doi = {10.1016/j.compbiomed.2013.07.035}

}

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

journal = {Computers in Biology and Medicine},

volume = {43},

author = {Corani, G. and Magli, M. and Giusti, A. and Gianaroli, L. and Gambardella, L.},

number = {11},

pages = {1783--1792},

year = {2013},

doi = {10.1016/j.compbiomed.2013.07.035}

}

(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},

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

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

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

pages = {233--243},

year = {2013}

}

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

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

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

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

pages = {233--243},

year = {2013}

}

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

@ARTICLE{corani2013d,

title = {Objective way to support embryo transfer: a probabilistic decision},

journal = {Human Reproduction},

volume = {28},

author = {Gianaroli, L. and Magli, M.C. and Gambardella, L. and Giusti, A. and Grugnetti, C. and Corani, G.},

number = {5},

pages = {1210--1220},

year = {2013},

doi = {10.1093/humrep/det030}

}

Downloadtitle = {Objective way to support embryo transfer: a probabilistic decision},

journal = {Human Reproduction},

volume = {28},

author = {Gianaroli, L. and Magli, M.C. and Gambardella, L. and Giusti, A. and Grugnetti, C. and Corani, G.},

number = {5},

pages = {1210--1220},

year = {2013},

doi = {10.1093/humrep/det030}

}

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

@ARTICLE{decampos2013c,

title = {Prognostic impact of monocyte count at presentation in mantle cell lymphoma},

journal = {British Journal of Haematology},

publisher = {Blackwell Publishing Ltd},

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.},

number = {4},

pages = {465--473},

year = {2013},

doi = {10.1111/bjh.12409},

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

}

Downloadtitle = {Prognostic impact of monocyte count at presentation in mantle cell lymphoma},

journal = {British Journal of Haematology},

publisher = {Blackwell Publishing Ltd},

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.},

number = {4},

pages = {465--473},

year = {2013},

doi = {10.1111/bjh.12409},

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

}

(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.

@INPROCEEDINGS{mangili2013a,

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

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

publisher = {SIPTA},

address = {Compiegne (FR)},

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

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

pages = {1--9},

year = {2013}

}

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

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

publisher = {SIPTA},

address = {Compiegne (FR)},

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

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

pages = {1--9},

year = {2013}

}

(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.

@INPROCEEDINGS{maua2013a,

title = {On the complexity of strong and epistemic credal networks},

publisher = {AUAI Press},

booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence},

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

pages = {391--400},

year = {2013}

}

Downloadtitle = {On the complexity of strong and epistemic credal networks},

publisher = {AUAI Press},

booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence},

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

pages = {391--400},

year = {2013}

}

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

@ARTICLE{maua2013b,

title = {On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables},

journal = {Artificial Intelligence},

volume = {205},

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

pages = {30--38},

year = {2013},

doi = {10.1016/j.artint.2013.10.002}

}

Downloadtitle = {On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables},

journal = {Artificial Intelligence},

volume = {205},

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

pages = {30--38},

year = {2013},

doi = {10.1016/j.artint.2013.10.002}

}

(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.

@INCOLLECTION{zaffalon2012a,

title = {Conglomerable coherent lower previsions},

editor = {Kruse, R. and Berthold, M. R. and Moewes, C. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer Berlin Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {190},

booktitle = {Synergies of Soft Computing and Statistics for Intelligent Data Analysis},

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

pages = {419--427},

year = {2013},

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

}

Downloadtitle = {Conglomerable coherent lower previsions},

editor = {Kruse, R. and Berthold, M. R. and Moewes, C. and Gil, M. A. and Grzegorzewski, P. and Hryniewicz, O.},

publisher = {Springer Berlin Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {190},

booktitle = {Synergies of Soft Computing and Statistics for Intelligent Data Analysis},

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

pages = {419--427},

year = {2013},

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

}

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

@ARTICLE{zaffalon2013b,

title = {Conglomerable coherence},

journal = {International Journal of Approximate Reasoning},

volume = {54},

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

number = {9},

pages = {1322--1350},

year = {2013},

doi = {10.1016/j.ijar.2013.04.016}

}

Downloadtitle = {Conglomerable coherence},

journal = {International Journal of Approximate Reasoning},

volume = {54},

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

number = {9},

pages = {1322--1350},

year = {2013},

doi = {10.1016/j.ijar.2013.04.016}

}

(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,

title = {Computing the conglomerable natural extension},

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

publisher = {SIPTA},

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

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

pages = {255--264},

year = {2013},

url = {http://www.sipta.org/isipta13/proceedings/papers/s025.pdf}

}

Downloadtitle = {Computing the conglomerable natural extension},

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

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pages = {255--264},

year = {2013},

url = {http://www.sipta.org/isipta13/proceedings/papers/s025.pdf}

}

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

@ARTICLE{zaffalon2013a,

title = {Probability and time},

journal = {Artificial Intelligence},

volume = {198},

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

pages = {1--51},

year = {2013},

doi = {10.1016/j.artint.2013.02.005}

}

Downloadtitle = {Probability and time},

journal = {Artificial Intelligence},

volume = {198},

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

pages = {1--51},

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doi = {10.1016/j.artint.2013.02.005}

}

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

## Compression-based AODE classifiers

## Bayesian networks with imprecise probabilities: theory and application to classification

## A Bayesian network model for predicting the outcome of in vitro fertilization

## A prognostic model for multiple-embryo transfers

## Solving limited memory influence diagrams

## Updating credal networks is approximable in polynomial time

## Anytime marginal map inference

## The complexity of approximately solving influence diagrams

## Credal model averaging: dealing robustly with model uncertainty on small data sets

## Conglomerable natural extension

## Evaluating credal classifiers by utility-discounted predictive accuracy

(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},

editor = {Denoeux, T. and Masson, M.H.},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {164},

booktitle = {Belief Functions: Theory and Applications},

author = {Antonucci, A.},

pages = {37--44},

year = {2012},

doi = {10.1007/978-3-642-29461-7_4}

}

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

editor = {Denoeux, T. and Masson, M.H.},

publisher = {Springer Berlin / Heidelberg},

series = {Advances in Intelligent and Soft Computing},

volume = {164},

booktitle = {Belief Functions: Theory and Applications},

author = {Antonucci, A.},

pages = {37--44},

year = {2012},

doi = {10.1007/978-3-642-29461-7_4}

}

(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.},

number = {5},

pages = {491--500},

year = {2012},

doi = {10.1007/978-3-642-31718-7_51}

}

Downloadtitle = {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.},

number = {5},

pages = {491--500},

year = {2012},

doi = {10.1007/978-3-642-31718-7_51}

}

(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)},

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

pages = {3--10},

year = {2012}

}

Downloadtitle = {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)},

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

pages = {3--10},

year = {2012}

}

(2012). Compression-based AODE classifiers. In De Raedt, L. et al. (Ed), *Proc. 20th European Conference on Artificial Intelligence (ECAI 2012)*, pp. 264–269.

@INPROCEEDINGS{corani2012d,

title = {Compression-based {AODE} classifiers},

editor = {De Raedt, L. et al. },

booktitle = {Proc. 20th European Conference on Artificial Intelligence ({ECAI} 2012)},

author = {Corani, G. and Antonucci, A. and De Rosa, R.},

pages = {264--269},

year = {2012}

}

Downloadtitle = {Compression-based {AODE} classifiers},

editor = {De Raedt, L. et al. },

booktitle = {Proc. 20th European Conference on Artificial Intelligence ({ECAI} 2012)},

author = {Corani, G. and Antonucci, A. and De Rosa, R.},

pages = {264--269},

year = {2012}

}

(2012). Bayesian networks with imprecise probabilities: theory and application to classification. In Holmes, D.E., Jain, L.C. (Eds), *Data Mining: Foundations and Intelligent Paradigms*, Intelligent Systems Reference Library **23**, Springer, Berlin / Heidelberg, pp. 49–93.

@INCOLLECTION{corani2012c,

title = {Bayesian networks with imprecise probabilities: theory and application to classification},

editor = {Holmes, D.E. and Jain, L.C.},

publisher = {Springer, Berlin / Heidelberg},

series = {Intelligent Systems Reference Library},

volume = {23},

booktitle = {Data Mining: Foundations and Intelligent Paradigms},

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

pages = {49--93},

year = {2012},

doi = {10.1007/978-3-642-23166-7_4}

}

Downloadtitle = {Bayesian networks with imprecise probabilities: theory and application to classification},

editor = {Holmes, D.E. and Jain, L.C.},

publisher = {Springer, Berlin / Heidelberg},

series = {Intelligent Systems Reference Library},

volume = {23},

booktitle = {Data Mining: Foundations and Intelligent Paradigms},

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

pages = {49--93},

year = {2012},

doi = {10.1007/978-3-642-23166-7_4}

}

(2012). A Bayesian network model for predicting the outcome of in vitro fertilization. In Cano, A., Gomez-Olmedo, M., Nielsen, T. (Eds), *Proc. of the 6th European Workshop on Probabilistic Graphical Models (PGM 2012)*, pp. 75–82.

@INPROCEEDINGS{corani2012e,

title = {A {B}ayesian network model for predicting the outcome of in vitro fertilization},

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

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

author = {Corani, G. and Magli, C. and Giusti, A. and Gianaroli, L. and Gambardella, L.},

pages = {75--82},

year = {2012}

}

Downloadtitle = {A {B}ayesian network model for predicting the outcome of in vitro fertilization},

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

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

author = {Corani, G. and Magli, C. and Giusti, A. and Gianaroli, L. and Gambardella, L.},

pages = {75--82},

year = {2012}

}

(2012). A prognostic model for multiple-embryo transfers. *Human Reproduction (Supplement: Abstract book, Proc. Annual Meeting ESHRE 2012)* **27**(2), pp. ii162–ii205.

@ARTICLE{corani2012b,

title = {A prognostic model for multiple-embryo transfers},

journal = {Human Reproduction (Supplement: Abstract {b}ook, Proc. Annual Meeting {ESHRE} 2012)},

volume = {27},

author = {Magli, C. and Corani, G. and Giusti, A. and Castelletti, E. and Gambardella, L. and Gianaroli, L.},

number = {2},

pages = {ii162--ii205},

year = {2012},

doi = {10.1093/humrep/27.s2.77}

}

Downloadtitle = {A prognostic model for multiple-embryo transfers},

journal = {Human Reproduction (Supplement: Abstract {b}ook, Proc. Annual Meeting {ESHRE} 2012)},

volume = {27},

author = {Magli, C. and Corani, G. and Giusti, A. and Castelletti, E. and Gambardella, L. and Gianaroli, L.},

number = {2},

pages = {ii162--ii205},

year = {2012},

doi = {10.1093/humrep/27.s2.77}

}

(2012). Solving limited memory influence diagrams. *Journal of Artificial Intelligence Research* **44**, pp. 97–140.

@ARTICLE{maua2012a,

title = {Solving limited memory influence diagrams},

journal = {Journal of Artificial Intelligence Research},

volume = {44},

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

pages = {97--140},

year = {2012},

url = {http://www.jair.org/media/3625/live-3625-6282-jair.pdf}

}

Downloadtitle = {Solving limited memory influence diagrams},

journal = {Journal of Artificial Intelligence Research},

volume = {44},

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

pages = {97--140},

year = {2012},

url = {http://www.jair.org/media/3625/live-3625-6282-jair.pdf}

}

(2012). Updating credal networks is approximable in polynomial time. *International Journal of Approximate Reasoning* **53**(8), pp. 1183–1199.

@ARTICLE{maua2012d,

title = {Updating credal networks is approximable in polynomial time},

journal = {International Journal of Approximate Reasoning},

volume = {53},

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

number = {8},

pages = {1183--1199},

year = {2012},

doi = {10.1016/j.ijar.2012.06.014},

url = {http://www.sciencedirect.com/science/article/pii/S0888613X12000904?v=s5}

}

Downloadtitle = {Updating credal networks is approximable in polynomial time},

journal = {International Journal of Approximate Reasoning},

volume = {53},

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

number = {8},

pages = {1183--1199},

year = {2012},

doi = {10.1016/j.ijar.2012.06.014},

url = {http://www.sciencedirect.com/science/article/pii/S0888613X12000904?v=s5}

}

(2012). Anytime marginal map inference. In *Proceedings of the 28th International Conference on Machine Learning (ICML 2012)*, pp. 1471–1478.

@INPROCEEDINGS{maua2012b,

title = {Anytime marginal map inference},

booktitle = {Proceedings of the 28th International Conference on Machine Learning ({ICML} 2012)},

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

pages = {1471--1478 },

year = {2012},

url = {http://icml.cc/2012/papers/728.pdf}

}

Downloadtitle = {Anytime marginal map inference},

booktitle = {Proceedings of the 28th International Conference on Machine Learning ({ICML} 2012)},

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

pages = {1471--1478 },

year = {2012},

url = {http://icml.cc/2012/papers/728.pdf}

}

(2012). The complexity of approximately solving influence diagrams. In *Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012)*, pp. 604–613.

@INPROCEEDINGS{maua2012c,

title = {The complexity of approximately solving influence diagrams},

booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence ({UAI} 2012)},

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

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Downloadtitle = {The complexity of approximately solving influence diagrams},

booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence ({UAI} 2012)},

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

pages = {604--613},

year = {2012},

url = {http://www.auai.org/uai2012/papers/166.pdf}

}

(2012). Credal model averaging: dealing robustly with model uncertainty on small data sets. In *Proc. 6th International Congress on Environmental Modelling and Software (iEMSs 2012)*, pp. 163-170.

@INCOLLECTION{corani2012a,

title = {Credal model averaging: dealing robustly with model uncertainty on small data sets},

booktitle = {Proc. 6th International Congress on Environmental Modelling and Software ({iEMSs} 2012)},

author = {Mignatti, A. and Corani, G. and Rizzoli, A.E.},

pages = {163-170},

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url = {http://www.iemss.org/iemss2012/proceedings/A3_0707_Mignatti_et_al.pdf}

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Downloadtitle = {Credal model averaging: dealing robustly with model uncertainty on small data sets},

booktitle = {Proc. 6th International Congress on Environmental Modelling and Software ({iEMSs} 2012)},

author = {Mignatti, A. and Corani, G. and Rizzoli, A.E.},

pages = {163-170},

year = {2012},

url = {http://www.iemss.org/iemss2012/proceedings/A3_0707_Mignatti_et_al.pdf}

}

(2012). Conglomerable natural extension. *International Journal of Approximate Reasoning* **53**(8), pp. 1200–1227.

@ARTICLE{zaffalon2012b,

title = {Conglomerable natural extension},

journal = {International Journal of Approximate Reasoning},

volume = {53},

author = {Miranda, E. and Zaffalon, M. and de Cooman, G.},

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Downloadtitle = {Conglomerable natural extension},

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pages = {1200--1227},

year = {2012},

doi = {10.1016/j.ijar.2012.06.015}

}

(2012). Evaluating credal classifiers by utility-discounted predictive accuracy. *International Journal of Approximate Reasoning* **53**(8), pp. 1282–1301.

@ARTICLE{zaffalon2012c,

title = {Evaluating credal classifiers by utility-discounted predictive accuracy},

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year = {2012},

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}

Downloadtitle = {Evaluating credal classifiers by utility-discounted predictive accuracy},

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volume = {53},

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number = {8},

pages = {1282--1301},

year = {2012},

doi = {10.1016/j.ijar.2012.06.022}

}

top## 2011

## The imprecise noisy-or gate

## Decision making by credal nets

## Likelihood-based naive credal classifier

## Action recognition by imprecise hidden Markov models

## New complexity results for MAP in Bayesian networks

## Inference with multinomial data: why to weaken the prior strength

## Efficient structure learning of Bayesian networks using constraints

## Bayesian networks and the imprecise Dirichlet model applied to recognition problems

## Independent natural extension

## Invariant Natural Killer T-cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and associates with the remission state

## Solving decision problems with limited information

## A fully polynomial time approximation scheme for updating credal networks of bounded treewidth and number of variable states

## Conglomerable natural extension

## Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome

## Utility-based accuracy measures to empirically evaluate credal classifiers

(2011). The imprecise noisy-or gate. In *FUSION 2011: Proceedings of the 14th International Conference on Information Fusion*, IEEE, pp. 709–715.

@INPROCEEDINGS{antonucci2011c,

title = {The imprecise noisy-or gate},

publisher = {IEEE},

booktitle = {{FUSION} 2011: Proceedings of the 14th International Conference on Information Fusion},

author = {Antonucci, A.},

pages = {709--715},

year = {2011}

}

Downloadtitle = {The imprecise noisy-or gate},

publisher = {IEEE},

booktitle = {{FUSION} 2011: Proceedings of the 14th International Conference on Information Fusion},

author = {Antonucci, A.},

pages = {709--715},

year = {2011}

}

(2011). Decision making by credal nets. In *Proceedings of the International Conference on Intelligent Human-machine Systems and Cybernetics (IHMSC 2011)* **1**, IEEE, Hangzhou (China), pp. 201–204.

@INPROCEEDINGS{antonucci2011d,

title = {Decision making by credal nets},

publisher = {IEEE},

address = {Hangzhou (China)},

volume = {1},

booktitle = {Proceedings of the International Conference on Intelligent Human-{m}achine Systems and Cybernetics ({IHMSC} 2011)},

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

pages = {201--204},

year = {2011}

}

Downloadtitle = {Decision making by credal nets},

publisher = {IEEE},

address = {Hangzhou (China)},

volume = {1},

booktitle = {Proceedings of the International Conference on Intelligent Human-{m}achine Systems and Cybernetics ({IHMSC} 2011)},

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

pages = {201--204},

year = {2011}

}

(2011). Likelihood-based naive credal classifier. In *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 21–30.

@INPROCEEDINGS{antonucci2011a,

title = {Likelihood-based naive credal classifier},

publisher = {SIPTA},

booktitle = {{ISIPTA} '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications},

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

pages = {21--30},

year = {2011},

url = {http://www.sipta.org/isipta11/proceedings/papers/s032.pdf}

}

Downloadtitle = {Likelihood-based naive credal classifier},

publisher = {SIPTA},

booktitle = {{ISIPTA} '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications},

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

pages = {21--30},

year = {2011},

url = {http://www.sipta.org/isipta11/proceedings/papers/s032.pdf}

}

(2011). Action recognition by imprecise hidden Markov models. In *Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, IPCV 2011*, CSREA Press, pp. 474–478.

@INPROCEEDINGS{antonucci2011b,

title = {Action recognition by imprecise hidden {M}arkov models},

publisher = {CSREA Press},

booktitle = {Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, {IPCV} 2011},

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

pages = {474--478},

year = {2011},

url = {http://www.lidi.info.unlp.edu.ar/WorldComp2011-Mirror/IPC5150.pdf}

}

Downloadtitle = {Action recognition by imprecise hidden {M}arkov models},

publisher = {CSREA Press},

booktitle = {Proceedings of the 2011 International Conference on Image Processing, Computer Vision and Pattern Recognition, {IPCV} 2011},

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

pages = {474--478},

year = {2011},

url = {http://www.lidi.info.unlp.edu.ar/WorldComp2011-Mirror/IPC5150.pdf}

}

(2011). New complexity results for MAP in Bayesian networks. In *International Joint Conference on Artificial Intelligence (IJCAI)*, AAAI Press, pp. 2100–2106.

@INPROCEEDINGS{decampos2011c,

title = {New complexity results for {MAP} in {B}ayesian networks},

publisher = {AAAI Press},

booktitle = {International Joint Conference on Artificial Intelligence ({IJCAI})},

author = {de Campos, C.P.},

pages = {2100--2106},

year = {2011},

url = {http://ijcai.org/papers11/Papers/IJCAI11-351.pdf}

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Downloadtitle = {New complexity results for {MAP} in {B}ayesian networks},

publisher = {AAAI Press},

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year = {2011},

url = {http://ijcai.org/papers11/Papers/IJCAI11-351.pdf}

}

(2011). Inference with multinomial data: why to weaken the prior strength. In *International Joint Conference on Artificial Intelligence (IJCAI)*, AAAI Press, pp. 2107–2112.

@INPROCEEDINGS{decampos2011e,

title = {Inference with multinomial data: why to weaken the prior strength},

publisher = {AAAI Press},

booktitle = {International Joint Conference on Artificial Intelligence ({IJCAI})},

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

pages = {2107--2112},

year = {2011},

url = {http://ijcai.org/papers11/Papers/IJCAI11-352.pdf}

}

Downloadtitle = {Inference with multinomial data: why to weaken the prior strength},

publisher = {AAAI Press},

booktitle = {International Joint Conference on Artificial Intelligence ({IJCAI})},

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

pages = {2107--2112},

year = {2011},

url = {http://ijcai.org/papers11/Papers/IJCAI11-352.pdf}

}

(2011). Efficient structure learning of Bayesian networks using constraints. *Journal of Machine Learning Research* **12**, pp. 663–689.

@ARTICLE{decampos2011a,

title = {Efficient structure learning of {B}ayesian networks using constraints},

journal = {Journal of Machine Learning Research},

volume = {12},

author = {de Campos, C.P. and Ji, Q.},

pages = {663--689},

year = {2011},

url = {http://jmlr.csail.mit.edu/papers/volume12/decampos11a/decampos11a.pdf}

}

Downloadtitle = {Efficient structure learning of {B}ayesian networks using constraints},

journal = {Journal of Machine Learning Research},

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pages = {663--689},

year = {2011},

url = {http://jmlr.csail.mit.edu/papers/volume12/decampos11a/decampos11a.pdf}

}

(2011). Bayesian networks and the imprecise Dirichlet model applied to recognition problems. In Liu, W. (Ed), *Symbolic and Quantitative Approaches to Reasoning With Uncertainty*, Lecture Notes in Computer Science **6717**, Springer, Berlin / Heidelberg, pp. 158–169.

@INPROCEEDINGS{decampos2011f,

title = {Bayesian networks and the imprecise {D}irichlet model applied to recognition problems},

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volume = {6717},

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

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Downloadtitle = {Bayesian networks and the imprecise {D}irichlet model applied to recognition problems},

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

author = {de Campos, C. and Ji, Q.},

pages = {158--169},

year = {2011},

doi = {10.1007/978-3-642-22152-1_14}

}

(2011). Independent natural extension. *Artificial Intelligence* **175**, pp. 1911–1950.

@ARTICLE{zaffalon2011a,

title = {Independent natural extension},

journal = {Artificial Intelligence},

volume = {175},

author = {de Cooman, G. and Miranda, E. and Zaffalon, M.},

pages = {1911--1950},

year = {2011},

doi = {10.1016/j.artint.2011.06.001}

}

Downloadtitle = {Independent natural extension},

journal = {Artificial Intelligence},

volume = {175},

author = {de Cooman, G. and Miranda, E. and Zaffalon, M.},

pages = {1911--1950},

year = {2011},

doi = {10.1016/j.artint.2011.06.001}

}

(2011). Invariant Natural Killer T-cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and associates with the remission state. *The Journal of Immunology* **186**(7), pp. 4490–4499.

@ARTICLE{azzimonti2011a,

title = {Invariant {N}atural {K}iller {T}-cell reconstitution in pediatric leukemia patients given {HLA}-haploidentical stem cell transplantation defines distinct {CD4+} and {CD4}- subset dynamics and associates with the remission state},

journal = {The Journal of Immunology},

volume = {186},

author = {de Lalla, C. and Rinaldi, A. and Montagna, D. and Azzimonti, L. and Bernardo, M.E. and Sangalli, L.M. and Paganoni, A.M. and Maccario, R. and Cesare-Merlone, A.D. and Zecca, M. and Locatelli, F. and Dellabona, P. and Casorati, G.},

number = {7},

pages = {4490--4499},

year = {2011},

doi = {10.4049/jimmunol.1003748}

}

Downloadtitle = {Invariant {N}atural {K}iller {T}-cell reconstitution in pediatric leukemia patients given {HLA}-haploidentical stem cell transplantation defines distinct {CD4+} and {CD4}- subset dynamics and associates with the remission state},

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volume = {186},

author = {de Lalla, C. and Rinaldi, A. and Montagna, D. and Azzimonti, L. and Bernardo, M.E. and Sangalli, L.M. and Paganoni, A.M. and Maccario, R. and Cesare-Merlone, A.D. and Zecca, M. and Locatelli, F. and Dellabona, P. and Casorati, G.},

number = {7},

pages = {4490--4499},

year = {2011},

doi = {10.4049/jimmunol.1003748}

}

(2011). Solving decision problems with limited information. In Shawe-Taylor, J., Zemel, R.S., Bartlett, P., Pereira, F.C.N., Weinberger, K.Q. (Eds), *Advances in Neural Information Processing Systems 24 (NIPS 2011)*, pp. 603–611.

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

pages = {603--611},

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url = {http://books.nips.cc/papers/files/nips24/NIPS2011_0422.pdf}

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(2011). A fully polynomial time approximation scheme for updating credal networks of bounded treewidth and number of variable states. In Coolen, F., de Cooman, G., Fetz, T., Oberguggenberger, M. (Eds), *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, Innsbruck, Austria, pp. 277–286.

@INPROCEEDINGS{maua2011b,

title = {A fully polynomial time approximation scheme for updating credal networks of bounded treewidth and number of variable states},

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Downloadtitle = {A fully polynomial time approximation scheme for updating credal networks of bounded treewidth and number of variable states},

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pages = {277--286},

year = {2011},

url = {http://leo.ugr.es/sipta/isipta11/proceedings/papers/s035.pdf}

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(2011). Conglomerable natural extension. In Coolen, F., de Cooman, G., Fetz, T., Oberguggenberger, M. (Eds), *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 287–296.

@INPROCEEDINGS{zaffalon2011c,

title = {Conglomerable natural extension},

editor = {Coolen, F. and de Cooman, G. and Fetz, T. and Oberguggenberger, M.},

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booktitle = {{ISIPTA} '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications},

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(2011). Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome. *Blood* **117**(5), pp. 1595–1604.

@ARTICLE{decampos2011b,

title = {Genome-wide {DNA} profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome},

journal = {Blood},

publisher = {The American Society of Hematology},

volume = {117},

author = {Rinaldi, A. and Mian, M. and Chigrinova, E. and Arcaini, L. and Bhagat, G. and Novak, U. and Rancoita, P.M.V. and Campos, C.P.D. and Forconi, F. and Gascoyne, R.D. and Facchetti, F. and Ponzoni, M. and Govi, S. and Ferreri, A.J.M. and Mollejo, M. and Piris, M.A. and Baldini, L. and Soulier, J. and Thieblemont, C. and Canzonieri, V. and Gattei, V. and Marasca, R. and Franceschetti, S. and Gaidano, G. and Tucci, A. and Uccella, S. and Tibiletti, M.G. and Dirnhofer, S. and Tripodo, C. and Doglioni, C. and Favera, R.D. and Cavalli, F. and Zucca, E. and Kwee, I. and Bertoni, F.},

number = {5},

pages = {1595--1604},

year = {2011},

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Downloadtitle = {Genome-wide {DNA} profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome},

journal = {Blood},

publisher = {The American Society of Hematology},

volume = {117},

author = {Rinaldi, A. and Mian, M. and Chigrinova, E. and Arcaini, L. and Bhagat, G. and Novak, U. and Rancoita, P.M.V. and Campos, C.P.D. and Forconi, F. and Gascoyne, R.D. and Facchetti, F. and Ponzoni, M. and Govi, S. and Ferreri, A.J.M. and Mollejo, M. and Piris, M.A. and Baldini, L. and Soulier, J. and Thieblemont, C. and Canzonieri, V. and Gattei, V. and Marasca, R. and Franceschetti, S. and Gaidano, G. and Tucci, A. and Uccella, S. and Tibiletti, M.G. and Dirnhofer, S. and Tripodo, C. and Doglioni, C. and Favera, R.D. and Cavalli, F. and Zucca, E. and Kwee, I. and Bertoni, F.},

number = {5},

pages = {1595--1604},

year = {2011},

doi = {10.1182/blood-2010-01-264275},

url = {http://bloodjournal.hematologylibrary.org/content/117/5/1595.full.pdf+html}

}

(2011). Utility-based accuracy measures to empirically evaluate credal classifiers. In Coolen, F., de Cooman, G., Fetz, T., Oberguggenberger, M. (Eds), *ISIPTA '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 401–410.

@INPROCEEDINGS{zaffalon2011b,

title = {Utility-based accuracy measures to empirically evaluate credal classifiers},

editor = {Coolen, F. and de Cooman, G. and Fetz, T. and Oberguggenberger, M.},

publisher = {SIPTA},

booktitle = {{ISIPTA} '11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications},

author = {Zaffalon, M. and Corani, G. and Mau\'a, D.D.},

pages = {401--410},

year = {2011},

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Downloadtitle = {Utility-based accuracy measures to empirically evaluate credal classifiers},

editor = {Coolen, F. and de Cooman, G. and Fetz, T. and Oberguggenberger, M.},

publisher = {SIPTA},

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author = {Zaffalon, M. and Corani, G. and Mau\'a, D.D.},

pages = {401--410},

year = {2011},

url = {http://www.sipta.org/isipta11/proceedings/papers/s016.pdf}

}

top## 2010

## Credal sets approximation by lower probabilities: application to credal networks

## Generalized loopy 2U: a new algorithm for approximate inference in credal networks

## Properties of Bayesian Dirichlet scores to learn Bayesian network structures

## An improved structural EM to learn dynamic Bayesian nets

## Epistemic irrelevance in credal nets: the case of imprecise markov trees

## Factorisation properties of the strong product

## Independent natural extension

## Restricting the IDM for classification

## A tree augmented classifier based on extreme imprecise Dirichlet model

## Robust texture recognition using credal classifiers

## Notes on desirability and conditional lower previsions

## Conditional models: coherence and inference through sequences of joint mass functions

## Inference and risk measurement with the pari-mutuel model

## Building knowledge-based expert systems by credal networks: a tutorial

## Genomic lesions associated with a different clinical outcome in diffuse large B-Cell lymphoma treated with R-CHOP-21

(2010). Credal sets approximation by lower probabilities: application to credal networks. In Hüllermeier, E., Kruse, R., Hoffmann, F. (Eds), *Computational Intelligence for Knowledge-based Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings*, Lecture Notes in Computer Science **6178**, Springer, pp. 716–725.

@INPROCEEDINGS{antonucci2010a,

title = {Credal sets approximation by lower probabilities: application to credal networks},

editor = {H\"ullermeier, E. and Kruse, R. and Hoffmann, F.},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

volume = {6178},

booktitle = {Computational Intelligence for Knowledge-{b}ased Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, {IPMU} 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings},

author = {Antonucci, A. and Cuzzolin, F.},

pages = {716--725},

year = {2010},

doi = {10.1007/978-3-642-14049-5_73}

}

Downloadtitle = {Credal sets approximation by lower probabilities: application to credal networks},

editor = {H\"ullermeier, E. and Kruse, R. and Hoffmann, F.},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

volume = {6178},

booktitle = {Computational Intelligence for Knowledge-{b}ased Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, {IPMU} 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings},

author = {Antonucci, A. and Cuzzolin, F.},

pages = {716--725},

year = {2010},

doi = {10.1007/978-3-642-14049-5_73}

}

(2010). Generalized loopy 2U: a new algorithm for approximate inference in credal networks. *International Journal of Approximate Reasoning* **55**(5), pp. 474–484.

@ARTICLE{antonucci2010c,

title = {Generalized loopy {2U}: a new algorithm for approximate inference in credal networks},

journal = {International Journal of Approximate Reasoning},

volume = {55},

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number = {5},

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doi = {10.1016/j.ijar.2010.01.007}

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Downloadtitle = {Generalized loopy {2U}: a new algorithm for approximate inference in credal networks},

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number = {5},

pages = {474--484},

year = {2010},

doi = {10.1016/j.ijar.2010.01.007}

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(2010). Properties of Bayesian Dirichlet scores to learn Bayesian network structures. In *AAAI Conference on Artificial Intelligence*, AAAI Press, pp. 431–436.

@INPROCEEDINGS{decampos2010c,

title = {Properties of {B}ayesian {D}irichlet scores to learn {B}ayesian network structures},

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url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1704/2013}

}

(2010). An improved structural EM to learn dynamic Bayesian nets. In *20th International Conference on Pattern Recognition (ICPR)*, pp. 601–604.

@INPROCEEDINGS{decampos2010d,

title = {An improved structural {EM} to learn dynamic {B}ayesian nets},

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(2010). Epistemic irrelevance in credal nets: the case of imprecise markov trees. *International Journal of Approximate Reasoning* **51**(9), pp. 1029–1052.

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(2010). Factorisation properties of the strong product. In Borgelt, C., González Rodrìguez, G., Trutschnig, W., Asunción Lubiano, M., Gil, M.A., Grzegorzewski, P., Hryniewicz, O. (Eds), *Combining Soft Computing and Statistical Methods in Data Analysis*, Advances in Intelligent and Soft Computing **77**, Springer, pp. 139--147.

@INPROCEEDINGS{zaffalon2010c,

title = {Factorisation properties of the strong product},

editor = {Borgelt, C. and Gonz\'alez Rodr\`iguez, G. and Trutschnig, W. and Asunci\'on Lubiano, M. and Gil, M.A. and Grzegorzewski, P. and Hryniewicz, O.},

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(2010). Independent natural extension. In Hüllermeier, E., Kruse, R., Hoffmann, F. (Eds), *Computational Intelligence for Knowledge-based Systems Design*, Lecture Notes in Computer Science **6178**, Springer, pp. 737–746.

@INPROCEEDINGS{zaffalon2010b,

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doi = {10.1007/978-3-642-14049-5_75}

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(2010). Restricting the IDM for classification. In Hullermeier, E., Kruse, R., Hoffmann, F. (Eds), *Information Processing and Management of Uncertainty in Knowledge-based Systems. Theory and Methods*, Communications in Computer and Information Science **80**, Springer, Berlin / Heidelberg, pp. 328–337.

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(2010). A tree augmented classifier based on extreme imprecise Dirichlet model. *International Journal of Approximate Reasoning* **51**(9), pp. 1053–1068.

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(2010). Robust texture recognition using credal classifiers. In Labrosse, F., Zwiggelaar, R., Liu, Y., Tiddeman, B. (Eds), *Proceedings of the British Machine Vision Conference*, BMVA Press, pp. 78.1–78.10.

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(2010). Notes on desirability and conditional lower previsions. *Annals of Mathematics and Artificial Intelligence* **60**(3–4), pp. 251–309.

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(2010). Conditional models: coherence and inference through sequences of joint mass functions. *Journal of Statistical Planning and Inference* **140**(7), pp. 1805–1833.

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(2010). Inference and risk measurement with the pari-mutuel model. *International Journal of Approximate Reasoning* **51**(9), pp. 1145–1158.

@ARTICLE{zaffalon2010d,

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(2010). Building knowledge-based expert systems by credal networks: a tutorial. In Baswell, A.R. (Ed), *Advances in Mathematics Research* **11**, Nova Science Publishers, New York.

@INBOOK{antonucci2010d,

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(2010). Genomic lesions associated with a different clinical outcome in diffuse large B-Cell lymphoma treated with R-CHOP-21. *British Journal of Haematology* **151**(3), pp. 221–231.

@ARTICLE{decampos2010a,

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volume = {151},

author = {Scandurra, M. and Mian, M. and Greiner, T.C. and Rancoita, P.M.V. and De Campos, C.P. and Chan, W.C. and Vose, J.M. and Chigrinova, E. and Inghirami, G. and Chiappella, A. and Baldini, L. and Ponzoni, M. and Ferreri, A.J.M. and Franceschetti, S. and Gaidano, G. and Montes-Moreno, S. and Piris, M.A. and Facchetti, F. and Tucci, A. and Nomdedeu, J.F. and Lazure, T. and Lambotte, O. and Uccella, S. and Pinotti, G. and Pruneri, G. and Martinelli, G. and Young, K.H. and Tibiletti, M.G. and Rinaldi, A. and Zucca, E. and Kwee, I. and Bertoni, F.},

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author = {Scandurra, M. and Mian, M. and Greiner, T.C. and Rancoita, P.M.V. and De Campos, C.P. and Chan, W.C. and Vose, J.M. and Chigrinova, E. and Inghirami, G. and Chiappella, A. and Baldini, L. and Ponzoni, M. and Ferreri, A.J.M. and Franceschetti, S. and Gaidano, G. and Montes-Moreno, S. and Piris, M.A. and Facchetti, F. and Tucci, A. and Nomdedeu, J.F. and Lazure, T. and Lambotte, O. and Uccella, S. and Pinotti, G. and Pruneri, G. and Martinelli, G. and Young, K.H. and Tibiletti, M.G. and Rinaldi, A. and Zucca, E. and Kwee, I. and Bertoni, F.},

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top## 2009

## Multiple model tracking by imprecise Markov trees

## Credal networks for military identification problems

## Modeling unreliable observations in Bayesian networks by credal networks

## Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence

## Structure learning of Bayesian networks using constraints

## Semi-qualitative probabilistic networks in computer vision problems

## Semi-qualitative probabilistic networks in computer vision problems

## Epistemic irrelevance in credal networks: the case of imprecise Markov trees

## A tree augmented classifier based on extreme imprecise Dirichlet model

## Reproducing human decisions in reservoir management: the case of lake lugano

## Lazy naive credal classifier

## Coherence graphs

(2009). Multiple model tracking by imprecise Markov trees. In *FUSION 2009: Proceedings of the 12th International Conference on Information Fusion*, IEEE.

@INPROCEEDINGS{antonucci2009e,

title = {Multiple model tracking by imprecise {M}arkov trees},

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author = {Antonucci, A. and Benavoli, A. and Zaffalon, M. and de Cooman, G. and Hermans, F.},

year = {2009},

url = {http://isif.org/fusion/proceedings/fusion09CD/data/papers/0478.pdf}

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year = {2009},

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(2009). Credal networks for military identification problems. *International Journal of Approximate Reasoning* **50**(2), pp. 666–679.

@ARTICLE{antonucci2009a,

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year = {2009},

doi = {10.1016/j.ijar.2009.01.005}

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number = {2},

pages = {666--679},

year = {2009},

doi = {10.1016/j.ijar.2009.01.005}

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(2009). Modeling unreliable observations in Bayesian networks by credal networks. In Godo, L., Pugliese, A. (Eds), *Scalable Uncertainty Management, Third International Conference, SUM 2009, Washington, DC, USA, September 28–30, 2009. Proceedings*, Lecture Notes in Computer Science **5785**, Springer, pp. 28–39.

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(2009). Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence. *Journal of Applied Logic* **7**(2), pp. 137–154.

@ARTICLE{decampos2009d,

title = {Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence},

journal = {Journal of Applied Logic},

volume = {7},

author = {de Campos, C.P. and Cozman, F.G. and Luna, J.E.O.},

number = {2},

pages = {137--154},

year = {2009},

doi = {10.1016/j.jal.2007.11.002}

}

Downloadtitle = {Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence},

journal = {Journal of Applied Logic},

volume = {7},

author = {de Campos, C.P. and Cozman, F.G. and Luna, J.E.O.},

number = {2},

pages = {137--154},

year = {2009},

doi = {10.1016/j.jal.2007.11.002}

}

(2009). Structure learning of Bayesian networks using constraints. In *International Conference on Machine Learning (ICML)* **382**, ACM, pp. 113–120.

@INPROCEEDINGS{decampos2009e,

title = {Structure learning of {B}ayesian networks using constraints},

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pages = {113--120},

year = {2009},

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}

(2009). Semi-qualitative probabilistic networks in computer vision problems. *Journal of Statistical Theory and Practice* **3**(1), pp. 197–210.

@ARTICLE{decampos2009c,

title = {Semi-qualitative probabilistic networks in computer vision problems},

journal = {Journal of Statistical Theory and Practice},

publisher = {Grace Scientific Publishing LLC},

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year = {2009},

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number = {1},

pages = {197--210},

year = {2009},

doi = {10.1080/15598608.2009.10411920}

}

(2009). Semi-qualitative probabilistic networks in computer vision problems. In Coolen-Schrijner, P., Coolen, F., Troffaes, M.C.M., Augustin, T. (Eds), *Imprecision in Statistical Theory and Practice.*, Grace Scientific Publishing LLC, Greensboro, North-Carolina, USA, pp. 207–220.

@INBOOK{decampos2009a,

title = {Semi-qualitative probabilistic networks in computer vision problems},

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editor = {Coolen-Schrijner, P. and Coolen, F. and Troffaes, M.C.M. and Augustin, T.},

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pages = {207--220},

year = {2009},

url = {http://www.amazon.com/Imprecision-Statistical-Practice-Pauline-Coolen-Schrijner/dp/0982399804}

}

(2009). Epistemic irrelevance in credal networks: the case of imprecise Markov trees. In Augustin, T., Coolen, F., Moral, S., Troffaes, M.C.M. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 149–158.

@INPROCEEDINGS{antonucci2009c,

title = {Epistemic irrelevance in credal networks: the case of imprecise {M}arkov trees},

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publisher = {SIPTA},

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author = {de Cooman, G. and Hermans, F. and Antonucci, A. and Zaffalon, M.},

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Downloadtitle = {Epistemic irrelevance in credal networks: the case of imprecise {M}arkov trees},

editor = {Augustin, T. and Coolen, F. and Moral, S. and Troffaes, M.C.M.},

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booktitle = {{ISIPTA} '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications},

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pages = {149--158},

year = {2009},

url = {http://www.sipta.org/isipta09/proceedings/papers/s053.pdf}

}

(2009). A tree augmented classifier based on extreme imprecise Dirichlet model. In Augustin, T., Coolen, F.P.A., Moral, S., Troffaes, M.C.M. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, Durham, UK, pp. 89–98.

@INPROCEEDINGS{corani2009c,

title = {A tree augmented classifier based on extreme imprecise {D}irichlet model},

editor = {Augustin, T. and Coolen, F.P.A. and Moral, S. and Troffaes, M.C.M.},

publisher = {SIPTA},

address = {Durham, UK},

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Downloadtitle = {A tree augmented classifier based on extreme imprecise {D}irichlet model},

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(2009). Reproducing human decisions in reservoir management: the case of lake lugano. In *Information Technologies in Environmental Engineering*, Springer, Berlin / Heidelberg, pp. 252–263.

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(2009). Lazy naive credal classifier. In *Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery From Uncertain Data*, U '09, ACM, New York, NY, USA, pp. 30–37.

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(2009). Coherence graphs. *Artificial Intelligence* **173**, pp. 104–144.

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(2009). Natural extension as a limit of regular extensions. In Augustin, T., Coolen, F., Troffaes, M.C.M., Moral, S. (Eds), *ISIPTA '09: Proceedings of the Sixth International Symposium on Imprecise Probability: Theories and Applications*, SIPTA, pp. 327–336.