Publications

Denis Deratani Mauá, Heitor Reis Ribeiro, Gustavo Perez Katague, Alessandro Antonucci (2020). Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks. PGM 2020.

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Lilith Mattei, Alessandro Antonucci, Denis Deratani Mauá, Alessandro Facchini, Julissa Villanueva Llerena (2020). Tractable inference in credal sentential decision diagrams. IJAR.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2020). Thirty years of credal networks: Specification, algorithms and complexity. IJAR.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2020). The Joy of Probabilistic Answer Set Programming: Semantics, complexity, expressivity, inference. IJAR.

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Caio Netto, Eduardo Tannuri, Denis Mauá, Fábio Cozman (2020). Prediction of Environmental Conditions for Maritime Navigation using a Network of Sensors: A Practical Application of Graph Neural Networks. KDMILE 2020.

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Renato Scaroni, Thiago P. Bueno, Leliane N. de Barros, Denis Mauá (2020). On the Performance of Planning Through Backpropagation. BRACIS 2020.

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Renato Geh, Denis Mauá, Alessandro Antonucci (2020). Learning Probabilistic Sentential Decision Diagrams by Sampling. KDMILE 2020.

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Milton Condori Fernandez, Leliane N. de Barros, Denis Mauá, Karina V. Delgado, Valdinei Freire (2020). Finding Feasible Policies for Extreme Risk-Averse Agents in Probabilistic Planning. BRACIS 2020.

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Julissa Villanueva Llerena, Denis Deratani Mauá (2020). Efficient Algorithms for Robustness Analysis of Maximum A Posteriori Inference in Selective Sum-Product Networks. IJAR.

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Ângelo G. Lovatto, Thiago P. Bueno, Denis D. Mauá, Leliane N. de Barros (2020). Decision-Aware Model Learning for Actor-Critic Methods: When Theory Does Not Meet Practice. ICBINB@NeurIPS 2020.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2020). Complexity results for probabilistic answer set programming. IJAR.

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Guilherme Thomaz, Denis Mauá, Leliane Barros (2020). A Contact Network-Based Approach for Online Planning of Containment Measures for COVID-19. ENIAC 2020.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2019). The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws. IJAR.

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Francisco H. O. Vieira de Faria, Arthur Colombini Gusmão, Glauber de Bona, Denis Deratani Mauá, Fabio Gagliardi Cozman (2019). Speeding up parameter and rule learning for acyclic probabilistic logic programs. IJAR.

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Julissa Villanueva Llerena, Denis Deratani Mauá (2019). Robust Analysis of MAP Inference in Selective Sum-Product Networks. ISPTA 2019.

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Lilith Mattei, Décio L. Soares, Alessandro Antonucci, Denis Deratani Mauá, Alessandro Facchini (2019). Exploring the Space of Probabilistic Sentential Decision Diagrams. 3rd Tractable Probabilistic Modeling Workshop.

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Renato Lui Geh, Denis Deratani Mauá (2019). End-To-End Imitation Learning of Lane Following Policies Using Sum-Product Networks. Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional (ENIAC).

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Thiago Pereira Bueno, Leliane Nunes de Barros, Denis Deratani Mauá, Scott Sanner (2019). Deep Reactive Policies for Planning in Stochastic Nonlinear Domains. AAAI 2019.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2018). The Finite Model Theory of Bayesian Networks: Descriptive Complexity. IJCAI.

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Ignasi Andrés, Leliane Nunes de Barros, Denis Deratani Mauá, Thiago Dias Simão (2018). When a Robot Reaches Out for Human Help. IBERAMIA.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2018). The complexity of Bayesian networks specified by propositional and relational languages. Artificial Intelligence.

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Denis Deratani Mauá, Diarmaid Conaty, Fabio Gagliardi Cozman, Katja Poppenhaeger, Cassio Polpo de Campos (2018). Robustifying sum-product networks. International Journal of Approximate Reasoning.

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Igor Cataneo Silveira, Denis Deratani Mauá (2018). Advances in Automatically Solving the ENEM. BRACIS 2018.

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Denis Deratani Mauá, Cassio P. de Campos (2017). Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks.

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Igor Cataneo Silveira, Denis Deratani Mauá (2017). University Entrance Exam as a Guiding Test for Artificial Intelligence. BRACIS 2017.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2017). The effect of combination functions on the complexity of relational Bayesian networks. International Journal of Approximate Reasoning.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2017). The Descriptive Complexity of Bayesian Network Specifications. ECSQARU.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2017). The Complexity of Inferences and Explanations in Probabilistic Logic Programming. ECSQARU.

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Francisco H. O. V. de Faria, Arthur C. Gusmão, Fábio Gagliardi Cozman, Denis Deratani Mauá (2017). Speeding-up ProbLog's Parameter Learning. Proceedings of the Seventh International Workshop on Statistical Relational AI.

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Francisco H. O. V. de Faria, Arthur C. Gusmão, Glauber de Bona, Denis Deratani Mauá, Fabio Gagliardi Cozman (2017). Parameter Learning in ProbLog with Probabilistic Rules. KDMILE.

Julissa Villanueva Llerena, Denis Deratani Mauá (2017). On Using Sum-Product Networks For Multi-Label Classification. BRACIS 2017.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2017). On the Semantics and Complexity of Probabilistic Logic Programs. JAIR.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2017). On the complexity of propositional and relational credal networks. IJAR.

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Thiago Pereira Bueno, Denis Deratani Mauá, Leliane Nunes de Barros, Fabio Gagliardi Cozman (2017). Modeling Markov Decision Processes with Imprecise Probabilities Using Probabilistic Logic Programming. ISIPTA 2017.

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Denis Deratani Mauá, Fabio Gagliardi Cozman, Diarmaid Conaty, Cassio Polpo de Campos (2017). Credal Sum-Product Networks. Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications.

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Francisco Henrique Otte Vieira de Faria, Fabio Gagliardi Cozman, Denis Deratani Mauá (2017). Closed-Form Solutions in Learning Probabilistic Logic Programs by Exact Score Maximization. Proceedings of the 11th International Conference on Scalable Uncertainty Management.

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Diarmaid Conaty, Denis Deratani Mauá, Cassio Polpo de Campos (2017). Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks. Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2016). The Complexity of Bayesian Networks Specified by Propositional and Relational Languages.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2016). The Well-Founded Semantics of Cyclic Probabilistic Logic Programs. XIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC).

Fabio Gagliardi Cozman, Denis Deratani Mauá (2016). The structure and complexity of credal semantics. Proceedings of the 3rd International Workshop on Probabilistic Logic Programming.

Denis Deratani Mauá, Fábio Gagliardi Cozman (2016). The Effect of Combination Functions on the Complexity of Relational Bayesian Networks. Proceedings of the Eighth International Conference on Probabilistic Graphical Models.

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Fábio Gagliardi Cozman, Denis Deratani Mauá (2016). Probabilistic Graphical Models Specified by Probabilistic Logic Programs: Semantics and Complexity. PGM.

Thiago Bueno, Denis Deratani Mauá, Leliane Nunes de Barros, Fabio Gagliardi Cozman (2016). Markov Decision Processes Specified by Probabilistic Logic Programming: Representation and Solution. BRACIS 2016.

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Walter Perez, Denis Deratani Mauá (2016). Improving Acyclic Selection Order-Based Bayesian Network Structure Learning. XIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC).

Denis Deratani Mauá, Alessandro Antonucci, Cassio Polpo de Campos (2016). Hidden Markov models with set-valued parameters. Neurocomputing.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2016). Fast local search methods for solving limited memory influence diagrams. International Journal of Approximate Reasoning.

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Denis Deratani Mauá (2016). Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams. IJAR.

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Walter Perez, Denis Deratani Mauá (2016). Better Initialization Heuristics for Order-based Bayesian Network Structure Learning. Journal of Information and Data Management.

Fábio Gagliardi Cozman, Denis Deratani Mauá (2015). The Complexity of Plate Probabilistic Models. Proceedings of the Ninth International Conference on Scalable Uncertainty Management.

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Denis Deratani Mauá, Cassio Polpo de Campos, Fabio Gagliardi Cozman (2015). The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages. IJCAI.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2015). Specifying Probabilistic Relational Models with Description Logics. Anais do XII Encontro Nacional de Inteligência Artificial e Computacional.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2015). On the Complexity of Propositional and Relational Credal Networks. Proceedings of the Ninth International Symposium on Imprecise Probability: Theories and Applications.

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Walter Perez Urcia, Denis Deratani Mauá (2015). Initialization Heuristics for Greedy Bayesian Network Structure Learning. Proceedings of the Third Symposium on Knowledge Discovery, Mining and Learning.

A. Antonucci, M. Scanagatta, D. Mauà, C.P. de Campos (2015). Early classification of time series by hidden Markov models with set-valued parameters. Proceedings of the NIPS Time Series Workshop.

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Denis Deratani Mauá, Fábio Gagliardi Cozman (2015). DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model. Proceedings of the Ninth International Conference on Scalable Uncertainty Management.

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Fabio Gagliardi Cozman, Denis Deratani Mauá (2015). Bayesian Networks Specified Using Propositional and Relational Constructs: Combined, Data, and Domain Complexity. AAAI.

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Fabio H. S. Machado, Fabio Gagliardi Cozman, Denis Deratani Mauá (2015). Bayesian Networks of Bounded Treewith: A Performance Analysis. Anais do XII Encontro Nacional de Inteligência Artificial e Computacional.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2015). A Tractable Class of Model Counting Problems. Anais do XII Encontro Nacional de Inteligência Artificial e Computacional.

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Giorgio Corani, Alessandro Antonucci, Denis Deratani Mauá, Sandra Gabaglio (2014). Trading off Speed and Accuracy in Multilabel Classification. PGM.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2014). Speeding Up k-Neighborhood Local Search in Limited Memory Influence Diagrams. PGM.

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Denis Deratani Mauá, Cassio Polpo de Campos, Alessio Benavoli, Alessandro Antonucci (2014). Probabilistic Inference in Credal Networks: New Complexity Results. Journal of Artificial Intelligence Research.

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Denis Deratani Mauá (2014). Equivalences between Maximum a Posteriori Inference in Bayesian Networks and Maximum Expected Utility Computation in Influence Diagrams. Proceedings of the 7th European Workshop on Probabilistic Graphical Models.

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Denis Deratani Mauá, Cassio Polpo de Campos, Alessandro Antonucci (2014). Algorithms for Hidden Markov Models With Imprecisely Specified Parameters. BRACIS.

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Siqi Nie, Denis Deratani Mauá, Cassio Polpo de Campos, Qiang Ji (2014). Advances in Learning Bayesian Networks of Bounded Treewidth. Advances in Neural Information Processing Systems 27.

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Siqi Nie, Denis Deratani Mauá, Cassio Polpo de Campos, Qiang Ji (2014). Advances in Learning Bayesian Networks of Bounded Treewidth.

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Denis Deratani Mauá, Cassio Polpo de Campos, Alessio Benavoli, Alessandro Antonucci (2013). On the Complexity of Strong and Epistemic Credal Networks. UAI.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marco Zaffalon (2013). On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables. Artificial Intelligence.

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Alessandro Antonucci, Giorgio Corani, Denis Deratani Mauá, Sandra Gabaglio (2013). An Ensemble of Bayesian Networks for Multilabel Classification. IJCAI 2015.

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Denis Deratani Mauá (2013). Algorithms and Complexity Results for Discrete Probabilistic Reasoning Tasks.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marco Zaffalon (2012). Updating Credal Networks is Approximable in Polynomial Time. International Journal of Approximate Reasoning.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marco Zaffalon (2012). The Complexity of Approximately Solving Influence Diagrams. UAI.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marcos Zaffalon (2012). Solving Limited Memory Influence Diagrams. Journal of Artificial Intelligence Research.

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Marco Zaffalon, Giorgio Corani, Denis Deratani Mauá (2012). Evaluating credal classifiers by utility-discounted predictive accuracy. International Journal of Approximate Reasoning.

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Denis Deratani Mauá, Cassio Polpo de Campos (2012). Anytime Marginal MAP Inference. ICML.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marco Zaffalon (2011). Solving Limited Memory Influence Diagrams. ArXiv e-prints.

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Marco Zaffalon, Giorgio Corani, Denis Deratani Mauá (2011). Utility-Based Accuracy Measures to Empirically Evaluate Credal Classifiers. ISIPTA ‘11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications.

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Denis Deratani Mauá, Cassio Polpo de Campos (2011). Solving Decision Problems with Limited Information. NIPS.

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Denis Deratani Mauá, Cassio Polpo de Campos, Marco Zaffalon (2011). A Fully Polynomial Time Approximation Scheme for Updating Credal Networks of Bounded Treewidth and Number of Variable States. ISIPTA ‘11: Proceedings of the Seventh International Symposium on Imprecise Probability: Theories and Applications.

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Denis Deratani Mauá (2009). Topic models on the automatic classification of user reviews.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2009). Representing and Classifying User Reviews. Encontro Nacional de Inteligência Artificial.

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2008). Using Social Data to Predict Trust on Web Communities : A Case Study with the Epinions.com Website. Workshop on Information Visualization and Analysis in Social Networks (WIVA).

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Denis Deratani Mauá, Fabio Gagliardi Cozman (2008). Managing Trust in Virtual Communities with Markov Logic. IV Workshop on MSc Dissertation and PhD Thesis in Artificial Intelligence (WTDIA).

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