56 citations · 103 across the 9 of their papers we have counts for
6 papers · 1 filter
Federated Fairness without Access to Sensitive Groups
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training. However, due to factors ranging from eme…
Minimax Demographic Group Fairness in Federated Learning
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness…
Distributionally Robust Group Backwards Compatibility
Martin Bertran, Natalia Martinez, Alex Oesterling +1
Machine learning models are updated as new data is acquired or new architectures are developed. These updates usually increase model performance, but may introduce backward compati…
Federating for Learning Group Fair Models
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minmax group fairness…
Instance based Generalization in Reinforcement Learning
Martin Bertran, Natalia Martinez, Mariano Phielipp +1
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.…
Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare
Natalia Martinez, Martin Bertran, Guillermo Sapiro
Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-popu…