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online-convex-optimization

topic1 events
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.

research/optimization/online-learning/arxiv