109 citations · 522 across the 51 of their papers we have counts for
Showing 2026Show all
2 papers · 1 filter
cs.LG2026
Avoiding unsafe sets when training with Langevin Dynamics
Adam M. Oberman
Training a model with noisy gradient descent can be idealized as overdamped Langevin dynamics, and a natural safety question is to bound the probability $ν_t(\mathcal{A}_H) = \math…
cs.LG2026
Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization
Adam M. Oberman
Self-supervised learning matches supervised accuracy from a fraction of the labels, but the labeled-sample efficiency behind this has lacked a theoretical explanation. We provide o…