Publications

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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