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Dataset Shift in Machine Learning$
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Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence

Print publication date: 2008

Print ISBN-13: 9780262170055

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262170055.001.0001

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An Adversarial View of Covariate Shift and a Minimax Approach

An Adversarial View of Covariate Shift and a Minimax Approach

Chapter:
(p.178) (p.179) 10 An Adversarial View of Covariate Shift and a Minimax Approach
Source:
Dataset Shift in Machine Learning
Author(s):

Globerson Amir

Hui Teo Choon

Smola Alex

Roweis Sam

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0010

This chapter considers an adversarial model where the learning algorithm attempts to construct a predictor that is robust to deletion of features at test time. The problem is formulated as finding the optimal minimax strategy with respect to an adversary which deletes features, and shows that the optimal strategy may be found by either solving a quadratic program or using efficient bundle methods for optimization. The resulting algorithm significantly improves prediction performance for several problems included in a spam-filtering challenge task.

Keywords:   learning algorithm, minimax problem, spam filtering, optimization

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