Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), pp. 6033-6043, 2017.

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Abstract:

Adversarial machines, where a learner competes against an adversary, have regained much recent interest in machine learning. They are naturally in the form of saddle-point optimization, often with separable structure but sometimes also with unmanageably large dimension. In this work we show that adversarial prediction under multivariate l...More

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