Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator

@InProceedings{pmlr-v97-yurtsever19b,
  title = 	 {Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator},
  author =       {Yurtsever, Alp and Sra, Suvrit and Cevher, Volkan},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {7282--7291},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--15 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/yurtsever19b/yurtsever19b.pdf},
  url = 	 {https://proceedings.mlr.press/v97/yurtsever19b.html},
  abstract = 	 {We propose a class of variance-reduced stochastic conditional gradient methods. By adopting the recent stochastic path-integrated differential estimator technique (SPIDER) of Fang et. al. (2018) for the classical Frank-Wolfe (FW) method, we introduce SPIDER-FW for finite-sum minimization as well as the more general expectation minimization problems. SPIDER-FW enjoys superior complexity guarantees in the non-convex setting, while matching the best known FW variants in the convex case. We also extend our framework a la conditional gradient sliding (CGS) of Lan & Zhou. (2016), and propose SPIDER-CGS.}
}