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New algorithms tackle prediction with expert advice under heavy-tailed losses

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for prediction with expert advice, specifically addressing scenarios with heavy-tailed losses. Existing methods can be suboptimal when losses have a bounded moment less than one, leading to polynomial dependence on the number of experts. The proposed Adaptive Heavy-Tailed FTRL and OMD algorithms achieve near-optimal regret guarantees without prior knowledge of loss parameters. Additionally, an FTRL algorithm with Shannon-entropy regularization offers optimal regret when these parameters are known. AI

IMPACT Introduces novel algorithmic approaches for improved prediction accuracy in machine learning contexts with heavy-tailed losses.

RANK_REASON Academic paper detailing new algorithms for prediction with expert advice. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithms tackle prediction with expert advice under heavy-tailed losses

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Academic paper detailing new algorithms for prediction with expert advice. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yuki Shibukawa, Shinji Ito ·

    Prediction with Expert Advice under Heavy-Tailed Losses

    arXiv:2610.14904v1 Announce Type: cross Abstract: We study prediction with expert advice over $T$ rounds with $K$ experts under heavy-tailed losses whose $(1+\epsilon)$-moment is bounded by $\theta>0$ for some $\epsilon\in(0,1]$. In this setting, existing algorithms designed for …

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