Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs

Abstract : In this paper, we propose several improvements on the block-coordinate Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to optimize the structured support vector machine (SSVM) objective in the context of structured prediction, though it has wider applications. The key intuition behind our improvements is that the estimates of block gaps maintained by BCFW reveal the block suboptimality that can be used as an adaptive criterion. First, we sample objects at each iteration of BCFW in an adaptive non-uniform way via gapbased sampling. Second, we incorporate pairwise and away-step variants of Frank-Wolfe into the block-coordinate setting. Third, we cache oracle calls with a cache-hit criterion based on the block gaps. Fourth, we provide the first method to compute an approximate regularization path for SSVM. Finally, we provide an exhaustive empirical evaluation of all our methods on four structured prediction datasets.
Type de document :
Communication dans un congrès
International Conference on Machine Learning (ICML 2016)., 2016, New York, United States. Appears in Proceedings of the 33rd International Conference on Machine Learning (ICML 2016)
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01323727
Contributeur : Jean-Baptiste Alayrac <>
Soumis le : mardi 31 mai 2016 - 10:10:09
Dernière modification le : jeudi 26 avril 2018 - 10:29:06

Lien texte intégral

Identifiants

  • HAL Id : hal-01323727, version 1
  • ARXIV : 1605.09346

Citation

Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet K. Dokania, Simon Lacoste-Julien. Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs. International Conference on Machine Learning (ICML 2016)., 2016, New York, United States. Appears in Proceedings of the 33rd International Conference on Machine Learning (ICML 2016). 〈hal-01323727〉

Partager

Métriques

Consultations de la notice

383