arXiv stat.ML
· Papers
A Geometric Approach to Constrained Online Learning
arXiv:2605.21107v3 Announce Type: replace-cross Abstract: We study constrained online convex optimization with adversarial time-varying constraints. At each round the learner acts before observing the loss and constraint, and is compared with the best fixed action satisfying all constraints in hindsight. The goal is to