32 citations · 35 across the 5 of their papers we have counts for
5 papers
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…
On the Generalization Error of Meta Learning for the Gibbs Algorithm
Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian +2
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm. Our exact characterization of the expected meta generalization error for t…
Tighter Expected Generalization Error Bounds via Convexity of Information Measures
Gholamali Aminian, Yuheng Bu, Gregory Wornell +1
Generalization error bounds are essential to understanding machine learning algorithms. This paper presents novel expected generalization error upper bounds based on the average jo…
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…
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…