An Adversarial View of Covariate Shift and a Minimax Approach
An Adversarial View of Covariate Shift and a Minimax Approach
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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