A nested-projection gradient algorithm attains O(log T) regret with O(log T) cumulative constraint violation for strongly convex losses, and O(√T) for both with convex losses; the body's proof is coherent, though the abstract claims lower-bound results the body never contains.
arXiv preprint arXiv:2303.01745 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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A modular reduction from budget-constrained contextual bandits with adversarial contexts to unconstrained bandits via surrogate rewards, yielding improved guarantees and an efficient algorithm based on SquareCB.
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A Geometric Approach to Constrained Online Learning
A nested-projection gradient algorithm attains O(log T) regret with O(log T) cumulative constraint violation for strongly convex losses, and O(√T) for both with convex losses; the body's proof is coherent, though the abstract claims lower-bound results the body never contains.
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Constrained Contextual Bandits with Adversarial Contexts
A modular reduction from budget-constrained contextual bandits with adversarial contexts to unconstrained bandits via surrogate rewards, yielding improved guarantees and an efficient algorithm based on SquareCB.