Generating Diverse Negations from Affirmative Sentences
A method for generating diverse, semantically controlled negations from affirmative sentences, probing and improving how language models handle negation.
Nov 19, 2024
Federated Fairness without Access to Sensitive Groups
Group fairness in federated learning when sensitive attributes cannot be observed: we protect worst-case subgroups via adversarially learned weightings, with guarantees and experiments on vision and tabular benchmarks.
Feb 22, 2024
Fair Federated Learning
Approaches for fairness and robustness in decentralized learning — with and without access to sensitive attributes — with formal guarantees, evaluated on computer vision and tabular data.
Oct 1, 2023
Minimax Demographic Group Fairness in Federated Learning
We formulate group fairness in federated learning as a minimax optimization over demographic group risks and provide algorithms with formal performance guarantees — protecting the worst-off group across heterogeneous clients.
Jun 20, 2022
Blind pareto fairness and subgroup robustness
A minimax framework that protects worst-case, unknown subgroups without demographic labels, trading off between Pareto efficiency and blind subgroup robustness.
Jun 10, 2021