Most existing works follow the scheme of selecting from a pre-generated pool of candidate prompts.

We provide the first tight characterization for the rate of the minimax simple regret by developing matching upper and lower bounds.

However, existing methods for evaluating this robustness metric are either computationally expensive or not very accurate.

Monte Carlo stopping rules in a manner that is both sample efficient and robust to estimation error.

Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data.