AI RESEARCH
Improved Guarantees for Constrained Online Convex Optimization via Self-Contraction
arXiv CS.LG
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ArXi:2605.21107v1 Announce Type: new We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before observing the loss and constraint function for that round. The goal is to achieve small static regret against the best point satisfying all constraints while also controlling cumulative constraint violation ($\mathsf