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Poster
Fast Differentiable Sorting and Ranking
Mathieu Blondel · Olivier Teboul · Quentin Berthet · Josip Djolonga

Tue Jul 14 02:00 PM -- 02:45 PM & Wed Jul 15 03:00 AM -- 03:45 AM (PDT) @
in Poster Session 8 »
The sorting operation is one of the most commonly used building blocks in computer programming. In machine learning, it is often used for robust statistics. However, seen as a function, it is piecewise linear and as a result includes many kinks where it is non-differentiable. More problematic is the related ranking operator, often used for order statistics and ranking metrics. It is a piecewise constant function, meaning that its derivatives are null or undefined. While numerous works have proposed differentiable proxies to sorting and ranking, they do not achieve the $O(n \log n)$ time complexity one would expect from sorting and ranking operations. In this paper, we propose the first differentiable sorting and ranking operators with $O(n \log n)$ time and $O(n)$ space complexity. Our proposal in addition enjoys exact computation and differentiation. We achieve this feat by constructing differentiable operators as projections onto the permutahedron, the convex hull of permutations, and using a reduction to isotonic optimization. Empirically, we confirm that our approach is an order of magnitude faster than existing approaches and showcase two novel applications: differentiable Spearman's rank correlation coefficient and least trimmed squares.
Keywords: Convex Optimization · Structured Prediction · Sequential, Network, and Time-Series Modeling

Author Information

Mathieu Blondel (Google)
Olivier Teboul (Google Brain)
Quentin Berthet (Google Brain)
Josip Djolonga (Google Research, Zurich)

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