Denis Deratani MauŠ
Denis Deratani MauŠ
Professor, Dept. of Computer Science, Institute of Mathematics and Statistics, Universidade de S„o
Verified email at usp.br - Homepage
TitleCited byYear
An Ensemble of Bayesian Networks for Multilabel Classification
A Alessandro, G Corani, D MauŠ, S Gabaglio
Twenty-Third International Joint Conference on Artificial Intelligence, 1220†…, 2013
43*2013
Evaluating credal classifiers by utility-discounted predictive accuracy
M Zaffalon, G Corani, D MauŠ
International Journal of Approximate Reasoning 53 (8), 1282-1301, 2012
382012
Solving limited memory influence diagrams
DD MauŠ, CP de Campos, M Zaffalon
Journal of Artificial Intelligence Research 44, 97-140, 2012
272012
Advances in learning Bayesian networks of bounded treewidth
S Nie, DD MauŠ, CP De Campos, Q Ji
Advances in Neural Information Processing Systems, 2285-2293, 2014
252014
Anytime Marginal MAP Inference
C Campos, DD Maua
29th International Conference on Machine Learning (ICML-12), 1471-1478, 2012
22*2012
Probabilistic inference in credal networks: new complexity results
DD MauŠ, CP De Campos, A Benavoli, A Antonucci
Journal of Artificial Intelligence Research 50, 603-637, 2014
212014
Trading off speed and accuracy in multilabel classification
G Corani, A Antonucci, DD MauŠ, S Gabaglio
European Workshop on Probabilistic Graphical Models, 145-159, 2014
162014
On the Complexity of Strong and Epistemic Credal Networks
DD MauŠ, CP de Campos, A Benavoli, A Antonucci
29th Conference on Uncertainty in Artificial Intelligence (UAI-13), 391-400, 2013
162013
Solving decision problems with limited information
DD MauŠ, C Campos
Advances in Neural Information Processing Systems (NIPS-11), 603-611, 2011
162011
Approximation complexity of maximum a posteriori inference in sum-product networks
D Conaty, DD MauŠ, CP De Campos
arXiv preprint arXiv:1703.06045, 2017
15*2017
Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity
FG Cozman, DD Maua
Twenty-Ninth AAAI Conference on Artificial Intelligence, 3519-3525, 2015
152015
Utility-based accuracy measures to empirically evaluate credal classifiers
M Zaffalon, G Corani, D MauŠ
Seventh International Symposium on Imprecise Probability: Theories and†…, 2011
152011
Equivalences between maximum a posteriori inference in bayesian networks and maximum expected utility computation in influence diagrams
DD MauŠ
International Journal of Approximate Reasoning 68, 211-229, 2016
142016
Updating credal networks is approximable in polynomial time
DD MauŠ, CP De Campos, M Zaffalon
International Journal of Approximate Reasoning 53 (8), 1183-1199, 2012
122012
The Complexity of Approximately Solving Influence Diagrams
DD MauŠ, CP de Campos, M Zaffalon
Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI-12†…, 2012
112012
On the semantics and complexity of probabilistic logic programs
FG Cozman, DD MauŠ
Journal of Artificial Intelligence Research 60, 221-262, 2017
92017
Hidden Markov models with set-valued parameters
DD Maua, A Antonucci, CP de Campos
Neurocomputing 180, 94-107, 2016
92016
Credal sum-product networks
DD MauŠ, FG Cozman, D Conaty, CP Campos
Proceedings of the Tenth International Symposium on Imprecise Probability†…, 2017
82017
The complexity of MAP inference in Bayesian networks specified through logical languages
DD MauŠ, CP De Campos, FG Cozman
International Joint Conference on Artificial Intelligence 24, 889-895, 2015
82015
On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables
DD MauŠ, CP De Campos, M Zaffalon
Artificial Intelligence 205, 30-38, 2013
82013
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