Paper "Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent" accepted at NIPS 2017

The following paper has been accepted as Oral Presentation  (40 / 3240 submissions) at the Thirty-first Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA , Dec. 4-9, 2017.

Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent by Xiangru Lian (University of Rochester), Ce Zhang (ETH Zurich), Huan Zhang (University of California, Davis), Cho-Jui Hsieh (University of California, Davis), Wei Zhang (IBM T.J. Watson Research Center) and Ji Liu (University of Rochester).