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M. Braverman

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#machine learning Preprint Oct 2026

Optimally Pacing Budget Spending and Learning

We establish near-optimal regret bounds for budget-constrained online learning against arbitrary classes of budget-pacing experts in the adversarial setting. In particular, given any class of $F$ experts and a candidate budget pacing schedule, we provide a full-information algorithm which obtains regret $O(D \sqrt{\log...

M. Braverman, Jing-Yi Liu, Jie-Ming Mao et al. · 0 citations