Denis Deratani Mauá

Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, 2024

2023

dPASP: A Comprehensive Differentiable Probabilistic Answer Set Programming Environment For Neurosymbolic Learning and Reasoning.

[BibT_eX]

[DOI]

Renato Lui Geh

Jonas Gonçalves

CoRR, 2023

Specifying credal sets with probabilistic answer set programming.

[BibT_eX]

[DOI]

Victor Hugo Nascimento Rocha

Proceedings of the International Symposium on Imprecise Probability: Theories and Applications, 2023

Assessing Good, Bad and Ugly Arguments Generated by ChatGPT: a New Dataset, its Methodology and Associated Tasks.

[BibT_eX]

[DOI]

Paulo Pirozelli

Proceedings of the Progress in Artificial Intelligence, 2023

2022

The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory.

[BibT_eX]

[DOI]

CoRR, 2022

Integrating Question Answering and Text-to-SQL in Portuguese.

[BibT_eX]

[DOI]

Marcos Menon José

Marcelo Archanjo José

Proceedings of the Computational Processing of the Portuguese Language, 2022

Time Robust Trees: Using Temporal Invariance to Improve Generalization.

[BibT_eX]

[DOI]

Luis Moneda

Proceedings of the Intelligent Systems - 11th Brazilian Conference, 2022

Exploration Versus Exploitation in Model-Based Reinforcement Learning: An Empirical Study.

[BibT_eX]

[DOI]

Ângelo Gregório Lovatto

Leliane Nunes de Barros

Daniel Augusto de Melo Moreira

Proceedings of the Intelligent Systems - 11th Brazilian Conference, 2022

2021

Efficient algorithms for Risk-Sensitive Markov Decision Processes with limited budget.

[BibT_eX]

[DOI]

Karina Valdivia Delgado

Leliane Nunes de Barros

Int. J. Approx. Reason., 2021

Special Issue on Robustness in Probabilistic Graphical Models.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2021

Learning probabilistic sentential decision diagrams under logic constraints by sampling and averaging.

[BibT_eX]

[DOI]

Renato Lui Geh

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Cautious Classification with Data Missing Not at Random Using Generative Random Forests.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2021

2020

Thirty years of credal networks: Specification, algorithms and complexity.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2020

Complexity results for probabilistic answer set programming.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2020

Tractable inference in credal sentential decision diagrams.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2020

Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2020

The joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2020

Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks.

[BibT_eX]

[DOI]

Heitor Ribeiro Reis

Gustavo Perez Katague

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice.

[BibT_eX]

[DOI]

Ângelo Gregório Lovatto

Thiago Pereira Bueno

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

On the Performance of Planning Through Backpropagation.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Systems - 9th Brazilian Conference, 2020

Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning.

[BibT_eX]

[DOI]

Milton Condori Fernandez

Karina Valdivia Delgado

Valdinei Freire

Proceedings of the Intelligent Systems - 9th Brazilian Conference, 2020

Efficient Predictive Uncertainty Estimators for Deep Probabilistic Models.

[BibT_eX]

[DOI]

Francisco H. O. V. de Faria

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Speeding up parameter and rule learning for acyclic probabilistic logic programs.

[BibT_eX]

[DOI]

Arthur Colombini Gusmão

Glauber De Bona

Int. J. Approx. Reason., 2019

The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws.

[BibT_eX]

[DOI]

Julissa Giuliana Villanueva Llerena

Int. J. Approx. Reason., 2019

Robust Analysis of MAP Inference in Selective Sum-Product Networks.

[BibT_eX]

[DOI]

Proceedings of the International Symposium on Imprecise Probabilities: Theories and Applications, 2019

Deep Reactive Policies for Planning in Stochastic Nonlinear Domains.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Robustifying sum-product networks.

[BibT_eX]

[DOI]

Diarmaid Conaty

Katja Poppenhaeger

Int. J. Approx. Reason., 2018

The complexity of Bayesian networks specified by propositional and relational languages.

[BibT_eX]

[DOI]

Artif. Intell., 2018

The Finite Model Theory of Bayesian Networks: Descriptive Complexity.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

When a Robot Reaches Out for Human Help.

[BibT_eX]

[DOI]

Ignasi Andrés

Leliane Nunes de Barros

Thiago D. Simão

Proceedings of the Advances in Artificial Intelligence - IBERAMIA 2018, 2018

Advances in Automatically Solving the ENEM.

[BibT_eX]

[DOI]

Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017

On the Semantics and Complexity of Probabilistic Logic Programs.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2017

The effect of combination functions on the complexity of relational Bayesian networks.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

On the complexity of propositional and relational credal networks.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks.

[BibT_eX]

[DOI]

Francisco H. O. V. de Faria

CoRR, 2017

Speeding-up ProbLog's Parameter Learning.

[BibT_eX]

[DOI]

Arthur C. Gusmão

CoRR, 2017

Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks.

[BibT_eX]

[DOI]

Diarmaid Conaty

Francisco Henrique Otte Vieira de Faria

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Closed-Form Solutions in Learning Probabilistic Logic Programs by Exact Score Maximization.

[BibT_eX]

[DOI]

Proceedings of the Scalable Uncertainty Management - 11th International Conference, 2017

Credal Sum-Product Networks.

[BibT_eX]

[DOI]

Diarmaid Conaty

Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, 2017

Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming.

[BibT_eX]

[DOI]

Thiago Pereira Bueno

Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, 2017

The Complexity of Inferences and Explanations in Probabilistic Logic Programming.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2017

The Descriptive Complexity of Bayesian Network Specifications.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2017

University Entrance Exam as a Guiding Test for Artificial Intelligence.

[BibT_eX]

[DOI]

Proceedings of the 2017 Brazilian Conference on Intelligent Systems, 2017

On Using Sum-Product Networks for Multi-label Classification.

[BibT_eX]

[DOI]

Proceedings of the 2017 Brazilian Conference on Intelligent Systems, 2017

2016

Better Initialization Heuristics for Order-based Bayesian Network Structure Learning.

[BibT_eX]

[DOI]

Walter Perez Urcia

J. Inf. Data Manag., 2016

Hidden Markov models with set-valued parameters.

[BibT_eX]

[DOI]

Neurocomputing, 2016

Fast local search methods for solving limited memory influence diagrams.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2016

Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2016

Probabilistic Graphical Models Specified by Probabilistic Logic Programs: Semantics and Complexity.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

The Structure and Complexity of Credal Semantics.

[BibT_eX]

[DOI]

Proceedings of the 3rd International Workshop on Probabilistic Logic Programming co-located with 26th International Conference on Inductive Logic Programming (ILP 2016), 2016

Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution.

[BibT_eX]

[DOI]

Thiago Pereira Bueno

Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

2015

DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model.

[BibT_eX]

[DOI]

Proceedings of the Scalable Uncertainty Management - 9th International Conference, 2015

The Complexity of Plate Probabilistic Models.

[BibT_eX]

[DOI]

Proceedings of the Scalable Uncertainty Management - 9th International Conference, 2015

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Probabilistic Inference in Credal Networks: New Complexity Results.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2014

Speeding Up k-Neighborhood Local Search in Limited Memory Influence Diagrams.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

Trading off Speed and Accuracy in Multilabel Classification.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

Advances in Learning Bayesian Networks of Bounded Treewidth.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Algorithms for Hidden Markov Models with Imprecisely Specified Parameters.

[BibT_eX]

[DOI]

Proceedings of the 2014 Brazilian Conference on Intelligent Systems, 2014

2013

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

[BibT_eX]

[DOI]

Artif. Intell., 2013

On the Complexity of Strong and Epistemic Credal Networks.

[BibT_eX]

[DOI]

Alessio Benavoli

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Approximation Algorithms for Max-Sum-Product Problems.

[BibT_eX]

[DOI]

Proceedings of the IJCAI 2013, 2013

An Ensemble of Bayesian Networks for Multilabel Classification.

[BibT_eX]

[DOI]

Proceedings of the IJCAI 2013, 2013

2012

Solving Limited Memory Influence Diagrams.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2012

Evaluating credal classifiers by utility-discounted predictive accuracy.

[BibT_eX]

[DOI]

Giorgio Corani

Int. J. Approx. Reason., 2012

Updating credal networks is approximable in polynomial time.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2012

The Complexity of Approximately Solving Influence Diagrams.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Anytime Marginal MAP Inference.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Solving Decision Problems with Limited Information.

[BibT_eX]

[DOI]