Preprint
- Federated Fairness without Access to Sensitive Groups
Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, Miguel Rodrigues
arXiv preprint, 2024 [Paper]
Conference Proceedings
-
Minimax Demographic Group Fairness in Federated Learning
Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, Miguel Rodrigues
2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2022) [Paper] [Code] -
Blind Pareto Fairness and Subgroup Robustness
Natalia Martinez, Martin Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro
Proceedings of the 38th International Conference on Machine Learning (ICML 2021) [Paper] [Code] -
Adversarially Learned Representations for Information Obfuscation and Inference
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, Guillermo Sapiro
36th International Conference on Machine Learning, (ICML 2019) [Paper] [Code]
Workshops
-
Generating Diverse Negations from Affirmative Sentences
Darian Rodriguez Vasquez, Afroditi Papadaki
Workshop on Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024) -
Federated Fairness without Access to Demographics
Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, Miguel Rodrigues
Workshop on Federated Learning: Recent Advances and New Challenges at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) [Paper] -
Federating for Learning Group Fair Models
Afroditi Papadaki, Natalia Martinez, Martin Bertran, Guillermo Sapiro, Miguel Rodrigues
New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) [Paper] -
Pareto Robustness for Fairness Beyond Demographics
Natalia Martinez, Martin Bertran, Afroditi Papadaki, Miguel Rodrigues, Guillermo Sapiro
Fair AI in Finance at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) [Video] -
Learning Representations for Utility and Privacy: An Information-Theoretic Based Approach
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Guillermo Sapiro
Privacy Preserving Machine Learning at the 30th Conference on Neural Information Processing Systems (NeurIPS 2018) [Paper] -
Learning and Deciding Our Own Privacy in a Collaborative System
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Guillermo Sapiro
Privacy in Machine Learning and Artificial Intelligence at the 35th International Conference on Machine Learning (ICML 2018)