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New theory improves Bayesian posterior adaptation for neural networks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new theoretical framework for adapting Bayesian posterior distributions in nonparametric settings. The study focuses on priors with p-exponential tails, demonstrating that contraction rates improve as 'p' decreases, leading to full adaptation to smoothness in a specific regime. This work has implications for understanding shallow ReLU neural networks, showing they can adapt to various regularity levels. AI

IMPACT Provides theoretical underpinnings for understanding and improving the adaptability of neural network models.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework and its applications.

Read on arXiv stat.ML →

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

New theory improves Bayesian posterior adaptation for neural networks

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Sergios Agapiou, Isma\"el Castillo, Paul Egels ·

    Leveraging tails for adaptation

    arXiv:2606.20480v1 Announce Type: cross Abstract: We consider contraction of Bayesian posterior distributions in nonparametric settings where coefficients of a function over a basis or dictionary are given priors with $p$--exponential tails, including Laplace tails $(p=1)$ and he…

  2. arXiv stat.ML TIER_1 English(EN) · Paul Egels ·

    Leveraging tails for adaptation

    We consider contraction of Bayesian posterior distributions in nonparametric settings where coefficients of a function over a basis or dictionary are given priors with $p$--exponential tails, including Laplace tails $(p=1)$ and heavier tails $(p<1)$. It is shown that contraction …

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