Consistent Polyhedral Surrogates for Top-k Classification and Variants

Anish Thilagar · Rafael Frongillo · Jessie Finocchiaro · Emma Goodwill

Hall E #1011

Keywords: [ MISC: Supervised Learning ] [ T: Learning Theory ]

Abstract: Top-$k$ classification is a generalization of multiclass classification used widely in information retrieval, image classification, and other extreme classification settings. Several hinge-like (piecewise-linear) surrogates have been proposed for the problem, yet all are either non-convex or inconsistent.For the proposed hinge-like surrogates that are convex (i.e., polyhedral), we apply the recent embedding framework of Finocchiaro et al.(2019; 2022) to determine the prediction problem for which the surrogate is consistent.These problems can all be interpreted as variants of top-$k$ classification, which may be better aligned with some applications.We leverage this analysis to derive constraints on the conditional label distributions under which these proposed surrogates become consistent for top-$k$.It has been further suggested that every convex hinge-like surrogate must be inconsistent for top-$k$.Yet, we use the same embedding framework to give the first consistent polyhedral surrogate for this problem.

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