| Adversarially learned representations for information obfuscation and inference M Bertran, N Martinez, A Papadaki, Q Qiu, M Rodrigues, G Reeves, ... International Conference on Machine Learning, 614-623, 2019 | 67 | 2019 |
| Blind pareto fairness and subgroup robustness NL Martinez, MA Bertran, A Papadaki, M Rodrigues, G Sapiro International Conference on Machine Learning, 7492-7501, 2021 | 35 | 2021 |
| Minimax Demographic Group Fairness in Federated Learning A Papadaki, N Martinez, M Bertran, G Sapiro, M Rodrigues 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22 …, 2022 | 34 | 2022 |
| Federating for learning group fair models A Papadaki, N Martinez, M Bertran, G Sapiro, M Rodrigues arXiv preprint arXiv:2110.01999, 2021 | 12 | 2021 |
| Learning to Collaborate for User-Controlled Privacy M Bertran, N Martinez, A Papadaki, Q Qiu, M Rodrigues, G Sapiro arXiv preprint arXiv:1805.07410, 2018 | 2 | 2018 |
| Federated Fairness without Access to Sensitive Groups A Papadaki, N Martinez, M Bertran, G Sapiro, M Rodrigues arXiv preprint arXiv:2402.14929, 2024 | 1 | 2024 |
| Federated Fairness without Access to Demographics A Papadaki, N Martinez, MA Bertran, G Sapiro, MRD Rodrigues Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022 | 1 | 2022 |
| Learning data-derived privacy preserving representations from information metrics M Bertran, N Martinez, A Papadaki, Q Qiu, M Rodrigues, G Sapiro | 1 | 2018 |
| Fair Federated Learning A Papadaki UCL (University College London), 2023 | | 2023 |
| Learning Representations for Utility and Privacy: An Information-Theoretic Based Approach M Bertran, N Martinez, A Papadaki, Q Qiu, M Rodrigues, G Sapiro | | |
Natalia Lucienne Martinez GilIBM ResearchVerified email at ibm.com
