Yves Rychener

2 accepted papers

2025

Global Group Fairness in Federated Learning via Function Tracking

AISTATS 2025poster

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probability metrics between distributions across a…

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2023

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Yves Rychener, Daniel Kuhn, Tobias Sutter

ICML 2023poster

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learn…

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