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.
Values are mean cost-weighted pseudo-regret ± standard error
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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.