no code implementations • 21 Oct 2019 • Chao Zhang, Jiahao Xie, Zebang Shen, Peilin Zhao, Tengfei Zhou, Hui Qian
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling.
no code implementations • ICML 2018 • Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian
Recently, the decentralized optimization problem is attracting growing attention.
no code implementations • 13 Nov 2016 • Zebang Shen, Hui Qian, Chao Zhang, Tengfei Zhou
Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emph{highly accurate solutions} to problems with \emph{a large number of samples} in \emph{ultra-high} dimensional space.
no code implementations • 12 Nov 2016 • Tengfei Zhou, Hui Qian, Zebang Shen, Congfu Xu
By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems.