Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds

Ziqiao Wang; Yongyi Mao

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arXiv · 2310.20102

Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds

Ziqiao Wang·Yongyi Mao

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Abstract

We present new information-theoretic generalization guarantees through the a novel construction of the "neighboring-hypothesis" matrix and a new family of stability notions termed sample-conditioned hypothesis (SCH) stability. Our approach yields sharper bounds that improve upon previous information-theoretic bounds in various learning scenarios. Notably, these bounds address the limitations of existing information-theoretic bounds in the context of stochastic convex optimization (SCO) problems, as explored in the recent work by Haghifam et al. (2023).

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BibTeX RIS

Ziqiao Wang, Yongyi Mao. 2023-10-31. Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds. https://arxiv.org/abs/2310.20102

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