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papersTODAY 04:00 UTC

Paper Proposes LEDGER Algorithm for Constrained Online Learning With Noisy Constraints

A new arXiv paper examines constrained online convex optimization where both constraint values and gradients are observed with noise. The authors introduce an algorithm called LEDGER, which they show achieves O(√T) expected regret and constraint violation under standard feasibility assumptions. The work targets settings with adversarial constraints and conditionally unbiased, finite-variance observations.

arXiv LEDGER constrained-online-learning noisy-constraints online-convex-optimization regret bounds

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIConstrained Online Learning with Noisy Constraint Values ↗TODAY 04:00 UTC

arXiv cs.LGConstrained Online Learning with Noisy Constraint Values ↗TODAY 04:00 UTC