We consider the problem of recovering the rank of a set of $n$ items based on noisy pairwise comparisons. We assume the SST class as the family of generative models. Our analysis gave sharp information theoretic upper and lower bound for the exact requirement, which matches exactly in the parametric limit. Our tight analysis on the algorithm induced by the moment method gave better constant in Minimax optimal rate than ~\citet{shah2017simple} and contribute to their open problem. The strategy we used in this work to obtain information theoretic bounds is based on combinatorial arguments and is of independent interest.
翻译:我们考虑的是恢复一套基于吵闹对称比较的零美元物品的等级的问题。我们假设SST类是基因模型的组合。我们的分析提供了精确要求的精确上下界限的精确信息理论性理论,该要求完全符合参数限制。我们对当时方法所引算的算法的严密分析使微米马克斯最佳率比 ⁇ citet{shah2017spsempro}更趋稳定,并助长了它们的公开问题。我们在这项工作中使用的获取信息理论界限的战略是基于组合论的论据,是独立的利益。