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New research explores robust PAC learning under Cressie--Read divergences

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper detailing the sample complexity of distributionally robust PAC learning, specifically focusing on Cressie--Read divergences. The study establishes new bounds for hypothesis classes with VC dimension, showing how adversarial perturbations affect learning rates. The findings reveal a complex interaction between statistical error estimation and robustness amplification, particularly in the agnostic learning case. AI

IMPACT Provides theoretical underpinnings for robust machine learning algorithms, potentially improving their performance in adversarial settings.

RANK_REASON The cluster contains an academic paper detailing theoretical research in machine learning.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores robust PAC learning under Cressie--Read divergences

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Elad Aigner-Horev, Daniel Rosenberg, Roi Weiss ·

    The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences

    arXiv:2608.04686v1 Announce Type: new Abstract: We study distributionally robust PAC learning for the $0$--$1$-loss, where adversarial perturbations of the data distribution are constrained by a Cressie--Read divergence of order $k>1$ and radius $\rho\geq 0$. For hypothesis class…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences

    We study distributionally robust PAC learning for the $0$--$1$-loss, where adversarial perturbations of the data distribution are constrained by a Cressie--Read divergence of order $k>1$ and radius $ρ\geq 0$. For hypothesis classes with VC dimension $d$, we establish realizable a…

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