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New LEDGER algorithm tackles noisy constraints in online optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called LEDGER for constrained online convex optimization, specifically addressing scenarios where constraint values and gradients are subject to adversarial noise. The algorithm aims to balance expected regret and expected budget violation, offering improved performance bounds compared to previous methods. LEDGER achieves competitive results across various parameter settings, without requiring a Slater condition, and also provides dynamic regret guarantees for predictable feasible comparator paths. AI

IMPACT Introduces a novel algorithm for optimizing systems with noisy data, potentially improving efficiency in various AI applications.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new algorithm for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New LEDGER algorithm tackles noisy constraints in online optimization

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The cluster contains a research paper published on arXiv detailing a new algorithm for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    Constrained Online Learning with Noisy Constraint Values

    arXiv:2609.06921v1 Announce Type: cross Abstract: We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise. Gaussian value noise of standard deviation $\sigma$ yields a worst-case lower b…

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