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cs.LG2026
Optimally Pacing Budget Spending and Learning
Mark Braverman, Jingyi Liu, Jieming Mao +2
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 a…
cs.LG2025
A New Benchmark for Online Learning with Budget-Balancing Constraints
Mark Braverman, Jingyi Liu, Jieming Mao +2
The adversarial Bandit with Knapsack problem is a multi-armed bandits problem with budget constraints and adversarial rewards and costs. In each round, a learner selects an action…