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New algorithm HT-PAder tackles online convex optimization with heavy-tailed noise

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HT-PAder, a novel parameter-free algorithm designed to tackle online convex optimization challenges in non-stationary environments with heavy-tailed noise. This new approach combines restarted AdaGrad experts with a pathwise meta-algorithm, AdaGrad-Hedge, which bypasses the need for moment conditions on meta-losses. HT-PAder achieves a near-optimal universal dynamic regret, outperforming previous methods even in scenarios with finite variance noise and without requiring prior knowledge of problem parameters. AI

IMPACT Introduces a new algorithmic approach for optimization problems common in machine learning.

RANK_REASON Academic paper detailing a new algorithm for online convex optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New algorithm HT-PAder tackles online convex optimization with heavy-tailed noise

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Academic paper detailing a new algorithm for online convex optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Vaneet Aggarwal ·

    Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise

    arXiv:2607.27073v1 Announce Type: new Abstract: We study online convex optimization (OCO) in non-stationary environments under heavy-tailed noise, where the stochastic gradient oracle admits only a finite $p$-th central moment for some $p \in (1, 2]$. While static regret is well-…

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