Fast and Scalable Structural SVM with Slack Rescaling


Heejin Choi, Ofer Meshi, Nathan Srebro ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:667-675, 2016.


We present an efficient method for training slack-rescaled structural SVM. Although finding the most violating label in a margin-rescaled formulation is often easy since the target function decomposes with respect to the structure, this is not the case for a slack-rescaled formulation, and finding the most violated label might be very difficult. Our core contribution is an efficient method for finding the most-violating-label in a slack-rescaled formulation, given an oracle that returns the most-violating-label in a (slightly modified) margin-rescaled formulation. We show that our method enables accurate and scalable training for slack-rescaled SVMs, reducing runtime by an order of magnitude compared to previous approaches to slack-rescaled SVMs.

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