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Learning Policies for Contextual Submodular Prediction (2013)

By: Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, and J. Andrew Bagnell

Abstract: Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optimal approach to optimizing such prediction problems based on no-regret learning. Our method leverages a surprising result from online submodular optimization: a single no-regret online learner can compete with an optimal sequence of predictions. Compared to previous work, which either learn a sequence of classifiers or rely on stronger assumptions such as realizability, we ensure both data-evaluatedciency as well as performance guarantees in the fully agnostic setting. Experiments validate the efficiency and applicability of the approach on a wide range of problems including manipulator trajectory optimization, news recommendation and document summarization.

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Stephane Ross, Jiaji Zhou, Yisong Yue, Debadeepta Dey, and J. Andrew Bagnell (2013). "Learning Policies for Contextual Submodular Prediction." International Conference on Machine Learning (ICML). pdf long          
BibTeX citation

@inproceedings{Ross+al:icml2013,
title = {Learning Policies for Contextual Submodular Prediction},
author = {Stephane Ross and Jiaji Zhou and Yisong Yue and Debadeepta Dey and J. Andrew Bagnell},
booktitle = {International Conference on Machine Learning (ICML)},
year = 2013,
wwwfilebase = {icml2013-ross-zhou-yue-dey-bagnell},
}



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