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New PAC-Bayes Derandomization Method for Smooth Loss Functions

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new method for derandomizing PAC-Bayes generalization bounds, specifically for smooth loss functions. This approach aims to create high-probability bounds for deterministic predictors by leveraging the smoothness properties of both the loss function and the predictor class. The study details the cost of transitioning from a Gibbs predictor to a deterministic predictor at the posterior mean, linking it to the generalization gap of the Jensen gap class, and proposes a practical regularizer inspired by the theoretical framework. AI

IMPACT This research could lead to more robust generalization bounds for machine learning models, potentially improving their reliability in real-world applications.

RANK_REASON The cluster contains an academic paper detailing a new theoretical method in machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New PAC-Bayes Derandomization Method for Smooth Loss Functions

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe Gigu\`ere ·

    Smoothness-Based Derandomization of PAC-Bayes Bounds

    arXiv:2606.19105v1 Announce Type: new Abstract: We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the pred…

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

    Smoothness-Based Derandomization of PAC-Bayes Bounds

    We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class. We show that passing from the Gibbs…

  3. arXiv stat.ML TIER_1 English(EN) · Philippe Giguère ·

    Smoothness-Based Derandomization of PAC-Bayes Bounds

    We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class. We show that passing from the Gibbs…

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