一种自适应采样策略，提升排名选择的精度与稳定性。
Annealed Entropic Allocation for Ranking and Selection
- 用加权软最小值替代硬切换，平滑处理近似活跃的竞争者。
- 在对称场景下性能优于传统方法，异构场景中结合鞍点修正更优。
- 适合需要高精度排名的选择任务，尤其适用于复杂分布环境。
我们提出了一种基于退火加权软最小值的自适应采样策略，用于静态预算分配问题。将最大化最小大偏差率目标替换为加权对数和指数近似，通过软最小权重融合各竞争者的成对得分，避免多个竞争者接近活跃时的硬切换问题。该近似引入了精细成对尾部渐近分析中的鞍点前因子，以捕捉超过主导指数之外的尾部行为。由于这些修正项为次指数级，随着预算增加降低退火温度仍能保持相同的首阶目标分配。对于静态问题，我们证明了对硬最小值的统一收敛性、软最小权重在活跃竞争者上的集中性，以及在固定权重下的目标分配映射连续性。实验表明，所提方法始终具有竞争力：无鞍点修正的变体在对称高斯与指数滑移设置中表现最佳，而鞍点加权在异构或非对称情况下更具优势。
原文摘要 · Abstract (English)
We propose annealed entropic allocation, an adaptive sampling policy based on an annealed, weighted soft-min formulation of static budget allocation. We replace the maximin large-deviation rate objective with a weighted log-sum-exp surrogate that blends challenger-specific pairwise scores through soft-min weights, avoiding hard switching when several challengers are nearly active. To capture tail behavior beyond the leading exponent, the surrogate incorporates saddlepoint prefactors from refined pairwise tail asymptotics. Because these corrections are subexponential, decreasing the annealing temperature with the budget preserves the same first-order target allocation. For the static problem, we prove uniform convergence to the hard minimum, concentration of soft-min weights on active challengers, and continuity of the induced target-allocation map under fixed weights. Experiments show that the proposed methods are consistently competitive: the no-saddlepoint ablation performs best in symmetric Gaussian and exponential slippage settings, while saddlepoint weighting can help in heterogeneous or asymmetric cases.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。