Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds

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).

Authors

• Ziqiao Wang University of Ottawa
• Yongyi Mao School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

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