Prudent-Banker achieves pseudo-regret Õ(√T + √D) and Õ(1) regret vs. safe comparator in adversarial bandits both with and without delays, matching new lower bounds up to logs.
Ucb revisited: Improved regret bounds for the stochastic multi-armed bandit problem.Periodica Mathematica Hungarica, 61(1-2):55–65
3 Pith papers cite this work. Polarity classification is still indexing.
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COMPASS-Hedge is presented as the first parameter-free full-information anytime algorithm that simultaneously delivers minimax-optimal adversarial regret, instance-optimal stochastic regret, and Õ(1) regret to a baseline policy.
Develops COF algorithm for MAB-CS that intelligently checks cheap arm feasibility by pooling samples, with generalized instance-dependent lower bounds and matching upper bounds on cumulative cost and quality regret.
citing papers explorer
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Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays
Prudent-Banker achieves pseudo-regret Õ(√T + √D) and Õ(1) regret vs. safe comparator in adversarial bandits both with and without delays, matching new lower bounds up to logs.
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Learning Safely Without Knowing the World:COMPASS-Hedge
COMPASS-Hedge is presented as the first parameter-free full-information anytime algorithm that simultaneously delivers minimax-optimal adversarial regret, instance-optimal stochastic regret, and Õ(1) regret to a baseline policy.
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Cost-Ordered Feasibility for Multi-Armed Bandits with Cost Subsidy
Develops COF algorithm for MAB-CS that intelligently checks cheap arm feasibility by pooling samples, with generalized instance-dependent lower bounds and matching upper bounds on cumulative cost and quality regret.