66 citations · 283 across the 24 of their papers we have counts for
5 papers · 1 filter
Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling
Kun Yuan, Bicheng Ying, Jiageng Liu +1
A new amortized variance-reduced gradient (AVRG) algorithm was developed in \cite{ying2017convergence}, which has constant storage requirement in comparison to SAGA and balanced gr…
Variance-Reduced Stochastic Learning under Random Reshuffling
Bicheng Ying, Kun Yuan, Ali H. Sayed
Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properti…
Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Case
Bicheng Ying, Ali H. Sayed
The analysis in Part I revealed interesting properties for subgradient learning algorithms in the context of stochastic optimization when gradient noise is present. These algorithm…
Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis
Kun Yuan, Bicheng Ying, Xiaochuan Zhao +1
Part I of this work [2] developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algo…
Exact Diffusion for Distributed Optimization and Learning --- Part I: Algorithm Development
Kun Yuan, Bicheng Ying, Xiaochuan Zhao +1
This work develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion st…