| Split conformal prediction for dependent data RI Oliveira, P Orenstein, T Ramos, JV Romano arXiv preprint arXiv:2203.15885, 2022 | 9 | 2022 |
| AmnioML: amniotic fluid segmentation and volume prediction with uncertainty quantification D Csillag, LM Paes, T Ramos, JV Romano, R Schuller, RB Seixas, ... Proceedings of the AAAI Conference on Artificial Intelligence 37 (13), 15494 …, 2023 | 3 | 2023 |
| BlockBoost: Scalable and Efficient Blocking through Boosting T Ramos, RL Schuller, AA Okuno, L Nissenbaum, RI Oliveira, P Orenstein International Conference on Artificial Intelligence and Statistics, 2575-2583, 2024 | | 2024 |
| Split Conformal Prediction and Non-Exchangeable Data RI Oliveira, P Orenstein, T Ramos, JV Romano In submission, 2024 | | 2024 |
| Boosting and concentration of measure methods in Machine Learning T Ramos https://impa.br/wp-content/uploads/2022/10/dout_tese_Thiago_Rodrigo_Ramos.pdf, 2022 | | 2022 |
| ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics D Csillag, C Piazza, T Ramos, JV Romano, RI Oliveira, P Orenstein International Conference on Artificial Intelligence and Statistics, 9017-9049, 2022 | | 2022 |
| Teoria ergódica em fluxos homogêneos e teoremas de Ratner TR Ramos Universidade de São Paulo, 2018 | | 2018 |
| Uncertainty Quantification for Amniotic Fluid Segmentation and Volume Prediction D Csillag, L Monteiro, T Ramos, JV Romano, R Schuller, RI Oliveira, ... | | |
| Optimizing Combinatorial and Non-decomposable Metrics with ExactBoost D Csillag, C Piazza, T Ramos, JV Romano, R Oliveira, P Orenstein | | |
Roberto Imbuzeiro OliveiraProfessor, IMPAVerified email at impa.br
