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Finally, a constrained MAB algorithm that gracefully degrades under adversarial constraint drift, achieving near-optimal regret when constraints are stochastic and smoothly transitioning to adversarial robustness.
Replicable algorithms can now achieve the same performance as non-replicable ones in constrained multi-armed bandit problems, opening the door to more reliable and reproducible online learning experiments.
