d9fc5b73a8d78fad3d6dffe419384e70-Reviews.html

Overview This paper proposes an algorithm for learning general structured predictors (e.g., non-linear). This is done by replacing the structured hinge loss with its smooth dual LP relaxation and observing that optimizing over classifiers reduces to a logistic regression task. Therefore, the learning problem can be extended to cases where this optimization over the class of predictors can be solved efficiently. Specifically, the paper shows how this enables learning predictors like decision trees and multi-layer perceptrons in addition to the common linear classifiers. Pros * The observation made by the authors about reduction of the learning objective to a logistic regression problem seems novel and interesting.