From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD

arXiv:2605.26222v1 Announce Type: new Abstract: Understanding the relationship between generalization and privacy remains a central challenge in modern machine learning theory, particularly for deep networks trained by variants of differentially private stochastic gradient descent (DP-SGD). In this work we make progress on this persistent open problem by proving a finite-sample bound on the approximate max-information of DP-SGD that exhibits scaling properties comparable with (Dwork et al, 2015)'s classic result for $\epsilon$-differentially private algorithms, namely at most linear in the dat

Source: arXiv cs.LG — read the full report at the original publisher.

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