2 papers
cs.LG2026
The geometry of invariant learning: an information-theoretic analysis of data augmentation and generalization
Abdelali Bouyahia, Frédéric LeBlanc, Mario Marchand
Data augmentation is one of the most widely used techniques to improve generalization in modern machine learning, often justified by its ability to promote invariance to label-irre…
cs.LG2024
Tighter Risk Bounds for Mixtures of Experts
Wissam Akretche, Frédéric LeBlanc, Mario Marchand
In this work, we provide upper bounds on the risk of mixtures of experts by imposing local differential privacy (LDP) on their gating mechanism. These theoretical guarantees are ta…