# Multiclass MinMax Rank Aggregation

## TL;DR

This paper introduces new minmax rank aggregation problems using Kendall tau and Spearman footrule distances, providing approximation algorithms and demonstrating their applications on Mallows model and genomic data.

## Contribution

It presents the first constant-approximation algorithms for NP-hard minmax rank aggregation problems under two distance measures.

## Key findings

- Algorithms achieve constant approximation ratios.
- Applications demonstrate effectiveness on real data.
- Framework applicable to various ranking scenarios.

## Abstract

We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall {\tau} and the Spearman footrule. As the problems are NP-hard, we proceed to describe a number of constant-approximation algorithms for solving them. We conclude with illustrative applications of the aggregation methods on the Mallows model and genomic data.