Learning Algorithm Generalization Error Bounds via Auxiliary Distributions

arXiv:2210.00483v2 Announce Type: replace-cross
Abstract: Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method, that leads to new upper bounds on expected generalization errors that are appropriate for supervised learning scenarios. We show that our general upper bounds can be specialized under some conditions to new bounds involving the $\alpha$-Jensen-Shannon, $\alpha$-R\'enyi ($0< \alpha < 1$) information between a random variable modeling the set …

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