SHPRR: An Algorithm for Pure Exploration under Constrained Regret

The paper introduces SHPRR, an algorithm that adapts Sequential Halving for pure exploration problems where regret relative to a baseline is a key cost metric. It shows that regret cost decreases as the algorithm progresses, allowing a front‑loaded allocation of the regret budget to improve best‑arm identification. Experiments on synthetic and real data demonstrate that SHPRR outperforms uniform regret allocation and existing baselines.

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