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cs.LG2025
Symmetries in PAC-Bayesian Learning
Armin Beck, Peter Ochs
Symmetries are known to improve the empirical performance of machine learning models, yet theoretical guarantees explaining these gains remain limited. Prior work has focused mainl…
math.OC2025
Bregman Stochastic Proximal Point Algorithm with Variance Reduction
Cheik Traoré, Peter Ochs
Stochastic algorithms, especially stochastic gradient descent (SGD), have proven to be the go-to methods in data science and machine learning. In recent years, the stochastic proxi…