Best-action queries yield Õ(min{T/k, √(T-k)}) regret for i.i.d. stochastic rewards but only Ω(√(T-k)) regret for correlated stochastic or adversarial rewards in the bandit-feedback model.
Exploration–exploitation tradeoff using variance estimates in multi-armed bandits.Theoretical Computer Science, 410(19):1876–1902
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TACC algorithm for adaptive multi-fidelity bandits with improving proxies achieves instance-dependent regret by replacing logarithmic high-fidelity pulls with bounded low-fidelity continuation for intermediate arms.
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Multi-Armed Bandits With Best-Action Queries
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Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies
TACC algorithm for adaptive multi-fidelity bandits with improving proxies achieves instance-dependent regret by replacing logarithmic high-fidelity pulls with bounded low-fidelity continuation for intermediate arms.
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