Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition

Efficient Rank Aggregation via Lehmer Codes

We propose a novel rank aggregation method based on converting permutations into their corresponding Lehmer codes or other subdiagonal images. Lehmer codes, also known as inversion vectors, are vector representations of permutations in which each coordinate can take values not restricted by the values of other coordinates. This transformation allows for decoupling of the coordinates and for performing aggregation via simple scalar median or mode computations. We present simulation results illustrating the performance of this completely parallelizable approach and analytically prove that both t…

Learning Mixtures of Permutations: Groups of Pairwise Comparisons and Combinatorial Method of Moments

In applications such as rank aggregation, mixture models for permutations are frequently used when the population exhibits heterogeneity. In this work, we study the widely used Mallows mixture model. In the high-dimensional setting, we propose a polynomial-time algorithm that learns a Mallows mixture of permutations on $n$ elements with the optimal sample complexity that is proportional to $\log n$, improving upon previous results that scale polynomially with $n$. In the high-noise regime, we characterize the optimal dependency of the sample complexity on the noise parameter. Both objectives a…

PPFS：预测排列特征选择

PPFS: Predictive Permutation Feature Selection

提出预测排列特征选择（PPFS），一种基于马尔可夫毯的新型包装器方法，利用预测排列独立性测试和监督算法，在分类和回归任务上优于现有方法。

Mallows排名的联邦聚合：Borda与Lehmer编码的比较分析

Federated Aggregation of Mallows Rankings: A Comparative Analysis of Borda and Lehmer Coding

本文首次提出基于Borda评分和Lehmer编码的联邦排名聚合方法，在Mallows模型下严格分析样本复杂度与通信复杂度，并比较两种方法的性能。