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Tese (Doutorado)
Qualitative global sensitivity analysis for probabilistic circuits (2023)
Llerena, Julissa Villanueva ; Mauá, Denis Deratani (Orientador)
Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LLERENA, Julissa Villanueva. Qualitative global sensitivity analysis for probabilistic circuits. 2023. Tese (Doutorado) – Universidade de São Paulo, São Paulo, 2023. Disponível em: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-25092023-112802/. Acesso em: 15 nov. 2025.
APA
Llerena, J. V. (2023). Qualitative global sensitivity analysis for probabilistic circuits (Tese (Doutorado). Universidade de São Paulo, São Paulo. Recuperado de https://www.teses.usp.br/teses/disponiveis/45/45134/tde-25092023-112802/
NLM
Llerena JV. Qualitative global sensitivity analysis for probabilistic circuits [Internet]. 2023 ;[citado 2025 nov. 15 ] Available from: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-25092023-112802/
Vancouver
Llerena JV. Qualitative global sensitivity analysis for probabilistic circuits [Internet]. 2023 ;[citado 2025 nov. 15 ] Available from: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-25092023-112802/
Trabalho de evento
Tractable classification with non-ignorable missing data using generative random forests (2022)
Villanueva Llerena, Julissa Giuliana ; Mauá, Denis Deratani
Source: Anais. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. 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
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani. Tractable classification with non-ignorable missing data using generative random forests. 2022, Anais.. Porto Alegre: SBC, 2022. Disponível em: https://doi.org/10.5753/kdmile.2022.227969. Acesso em: 15 nov. 2025.
APA
Villanueva Llerena, J. G., & Mauá, D. D. (2022). Tractable classification with non-ignorable missing data using generative random forests. In Anais. Porto Alegre: SBC. doi:10.5753/kdmile.2022.227969
NLM
Villanueva Llerena JG, Mauá DD. Tractable classification with non-ignorable missing data using generative random forests [Internet]. Anais. 2022 ;[citado 2025 nov. 15 ] Available from: https://doi.org/10.5753/kdmile.2022.227969
Vancouver
Villanueva Llerena JG, Mauá DD. Tractable classification with non-ignorable missing data using generative random forests [Internet]. Anais. 2022 ;[citado 2025 nov. 15 ] Available from: https://doi.org/10.5753/kdmile.2022.227969
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: 15 nov. 2025.
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 2025 nov. 15 ] 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 2025 nov. 15 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21
Trabalho de evento-resumo periodico
Efficient predictive uncertainty estimators for deep probabilistic models (2020)
Llerena, Julissa Villanueva ; Mauá, Denis Deratani
Source: Proceddings : AAAI-20 Student Tracks. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LLERENA, Julissa Villanueva e MAUÁ, Denis Deratani. Efficient predictive uncertainty estimators for deep probabilistic models. Proceddings : AAAI-20 Student Tracks. Palo Alto: AAAI Press. Disponível em: https://doi.org/10.1609/aaai.v34i10.7142. Acesso em: 15 nov. 2025. , 2020
APA
Llerena, J. V., & Mauá, D. D. (2020). Efficient predictive uncertainty estimators for deep probabilistic models. Proceddings : AAAI-20 Student Tracks. Palo Alto: AAAI Press. doi:10.1609/aaai.v34i10.7142
NLM
Llerena JV, Mauá DD. Efficient predictive uncertainty estimators for deep probabilistic models [Internet]. Proceddings : AAAI-20 Student Tracks. 2020 ; 35( 100): 13740-13741.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1609/aaai.v34i10.7142
Vancouver
Llerena JV, Mauá DD. Efficient predictive uncertainty estimators for deep probabilistic models [Internet]. Proceddings : AAAI-20 Student Tracks. 2020 ; 35( 100): 13740-13741.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1609/aaai.v34i10.7142
Artigo de periodico
Tractable inference in credal sentential decision diagrams (2020)
Mattei, Lilith; Antonucci, Alessandro; Mauá, Denis Deratani ; Facchini, Alessandro ; Villanueva Llerena, Julissa Giuliana
Source: International Journal of Approximate Reasoning. Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MATTEI, Lilith et al. Tractable inference in credal sentential decision diagrams. International Journal of Approximate Reasoning, v. 125, p. 26-48, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.06.005. Acesso em: 15 nov. 2025.
APA
Mattei, L., Antonucci, A., Mauá, D. D., Facchini, A., & Villanueva Llerena, J. G. (2020). Tractable inference in credal sentential decision diagrams. International Journal of Approximate Reasoning, 125, 26-48. doi:10.1016/j.ijar.2020.06.005
NLM
Mattei L, Antonucci A, Mauá DD, Facchini A, Villanueva Llerena JG. Tractable inference in credal sentential decision diagrams [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 26-48.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.ijar.2020.06.005
Vancouver
Mattei L, Antonucci A, Mauá DD, Facchini A, Villanueva Llerena JG. Tractable inference in credal sentential decision diagrams [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 26-48.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.ijar.2020.06.005
Artigo de periodico
Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks (2020)
Villanueva Llerena, Julissa ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. 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
VILLANUEVA LLERENA, Julissa e MAUÁ, Denis Deratani. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks. International Journal of Approximate Reasoning, v. 126, p. 158-180-, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.07.008. Acesso em: 15 nov. 2025.
APA
Villanueva Llerena, J., & Mauá, D. D. (2020). Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks. International Journal of Approximate Reasoning, 126, 158-180-. doi:10.1016/j.ijar.2020.07.008
NLM
Villanueva Llerena J, Mauá DD. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 158-180-.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.008
Vancouver
Villanueva Llerena J, Mauá DD. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 158-180-.[citado 2025 nov. 15 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.008

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