Scalable learning of non-decomposable objectives

Elad Eban, Mariano Schain, Alan Mackey, Ariel Gordon, Rif A. Saurous, Gal Elidan

Research output: Contribution to conferencePaperpeer-review

63 Scopus citations

Abstract

Modern retrieval systems are often driven by an underlying machine learning model. The goal of such systems is to identify and possibly rank the few most relevant items for a given query or context. Thus, such systems are typically evaluated using a ranking-based performance metric such as the area under the precision-recall curve, the Fβ score, precision at fixed recall, etc. Obviously, it is desirable to train such systems to optimize the metric of interest. In practice, due to the scalability limitations of existing approaches for optimizing such objectives, large-scale retrieval systems are instead trained to maximize classification accuracy, in the hope that performance as measured via the true objective will also be favorable. In this work we present a unified framework that, using straightforward building block bounds, allows for highly scalable optimization of a wide range of ranking-based objectives. We demonstrate the advantage of our approach on several real-life retrieval problems that are significantly larger than those considered in the literature, while achieving substantial improvement in performance over the accuracy-objective baseline.

Original languageAmerican English
StatePublished - 2017
Externally publishedYes
Event20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States
Duration: 20 Apr 201722 Apr 2017

Conference

Conference20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
Country/TerritoryUnited States
CityFort Lauderdale
Period20/04/1722/04/17

Bibliographical note

Publisher Copyright:
Copyright 2017 by the author(s).

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