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Trabalho de evento
Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments (2021)
Lovatto, Ângelo Gregório ; Bueno, Thiago Pereira ; Barros, Leliane Nunes de
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório e BUENO, Thiago Pereira e BARROS, Leliane Nunes de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments. 2021, Anais.. Cham: Springer, 2021. Disponível em: https://doi.org/10.1007/978-3-030-91702-9_3. Acesso em: 19 nov. 2024.
APA
Lovatto, Â. G., Bueno, T. P., & Barros, L. N. de. (2021). Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-91702-9_3
NLM
Lovatto ÂG, Bueno TP, Barros LN de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments [Internet]. Proceedings. 2021 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_3
Vancouver
Lovatto ÂG, Bueno TP, Barros LN de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments [Internet]. Proceedings. 2021 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_3
Trabalho de evento
Cautious classification with data missing not at random using generative random forests (2021)
Villanueva Llerena, Julissa Giuliana ; Mauá, Denis Deratani ; Antonucci, Alessandro
Source: Proceedings. Conference titles: European Conference on Symbolic and Quantitative Approaches with Uncertainty - ECSQARU. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani e ANTONUCCI, Alessandro. Cautious classification with data missing not at random using generative random forests. 2021, Anais.. Cham: Springer, 2021. Disponível em: https://doi.org/10.1007/978-3-030-86772-0_21. Acesso em: 19 nov. 2024.
APA
Villanueva Llerena, J. G., Mauá, D. D., & Antonucci, A. (2021). Cautious classification with data missing not at random using generative random forests. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-86772-0_21
NLM
Villanueva Llerena JG, Mauá DD, Antonucci A. Cautious classification with data missing not at random using generative random forests [Internet]. Proceedings. 2021 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21
Vancouver
Villanueva Llerena JG, Mauá DD, Antonucci A. Cautious classification with data missing not at random using generative random forests [Internet]. Proceedings. 2021 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21
Trabalho de evento
Finding feasible policies for extreme risk-averse agents in probabilistic planning (2020)
Fernandez, Milton Condori; Barros, Leliane Nunes de ; Mauá, Denis Deratani ; Delgado, Karina Valdivia ; Silva, Valdinei Freire da
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: IME, EACH
Subjects: PROBABILIDADE, MODELOS PARA PROCESSOS ESTOCÁSTICOS, ANÁLISE DE RISCO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDEZ, Milton Condori et al. Finding feasible policies for extreme risk-averse agents in probabilistic planning. 2020, Anais.. Cham: Springer, 2020. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_7. Acesso em: 19 nov. 2024.
APA
Fernandez, M. C., Barros, L. N. de, Mauá, D. D., Delgado, K. V., & Silva, V. F. da. (2020). Finding feasible policies for extreme risk-averse agents in probabilistic planning. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-61380-8_7
NLM
Fernandez MC, Barros LN de, Mauá DD, Delgado KV, Silva VF da. Finding feasible policies for extreme risk-averse agents in probabilistic planning [Internet]. Proceedings. 2020 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_7
Vancouver
Fernandez MC, Barros LN de, Mauá DD, Delgado KV, Silva VF da. Finding feasible policies for extreme risk-averse agents in probabilistic planning [Internet]. Proceedings. 2020 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_7
Trabalho de evento
DL-Lite Bayesian networks: a tractable probabilistic graphical model (2015)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: Scalable Uncertainty Management: 9th International Conference, SUM 2015, Québec City, QC, Canada, September 16-18, 2015. Proceedings. Conference titles: International Conference on Scalable Uncertainty Management - SUM. Unidades: IME, EP
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, COMPUTABILIDADE E COMPLEXIDADE, INTELIGÊNCIA ARTIFICIAL, PROBABILIDADE APLICADA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. DL-Lite Bayesian networks: a tractable probabilistic graphical model. 2015, Anais.. Cham: Springer, 2015. Disponível em: https://doi.org/10.1007/978-3-319-23540-0_4. Acesso em: 19 nov. 2024.
APA
Mauá, D. D., & Cozman, F. G. (2015). DL-Lite Bayesian networks: a tractable probabilistic graphical model. In Scalable Uncertainty Management: 9th International Conference, SUM 2015, Québec City, QC, Canada, September 16-18, 2015. Proceedings. Cham: Springer. doi:10.1007/978-3-319-23540-0_4
NLM
Mauá DD, Cozman FG. DL-Lite Bayesian networks: a tractable probabilistic graphical model [Internet]. Scalable Uncertainty Management: 9th International Conference, SUM 2015, Québec City, QC, Canada, September 16-18, 2015. Proceedings. 2015 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-319-23540-0_4
Vancouver
Mauá DD, Cozman FG. DL-Lite Bayesian networks: a tractable probabilistic graphical model [Internet]. Scalable Uncertainty Management: 9th International Conference, SUM 2015, Québec City, QC, Canada, September 16-18, 2015. Proceedings. 2015 ;[citado 2024 nov. 19 ] Available from: https://doi.org/10.1007/978-3-319-23540-0_4

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