7 citations · 12 across the 5 of their papers we have counts for
5 papers
Aggregated Gradient Langevin Dynamics
Chao Zhang, Jiahao Xie, Zebang Shen +3
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic conver…
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou +2
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate q…
Accelerated Variance Reduced Block Coordinate Descent
Zebang Shen, Hui Qian, Chao Zhang +1
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 \emp…
Accelerated Stochastic ADMM with Variance Reduction
Chao Zhang, Zebang Shen, Hui Qian +3
Alternating Direction Method of Multipliers (ADMM) is a popular method for solving large-scale Machine Learning problems. Stochastic ADMM was proposed to reduce the per iteration c…
Riemannian Tensor Completion with Side Information
Tengfei Zhou, Hui Qian, Zebang Shen +1
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 pro…