42 citations · 163 across the 36 of their papers we have counts for
5 papers · 1 filter
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction
Haishan Ye, Wei Xiong, Tong Zhang
This paper considers the decentralized composite optimization problem. We propose a novel decentralized variance-reduction proximal-gradient algorithmic framework, called PMGT-VR,…
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices
Luo Luo, Cheng Chen, Guangzeng Xie +1
We study the streaming model for approximate matrix multiplication (AMM). We are interested in the scenario that the algorithm can only take one pass over the data with limited mem…
Multi-consensus Decentralized Accelerated Gradient Descent
Haishan Ye, Luo Luo, Ziang Zhou +1
This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We…
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation
Chaoyang He, Haishan Ye, Li Shen +1
Many recently proposed methods for Neural Architecture Search (NAS) can be formulated as bilevel optimization. For efficient implementation, its solution requires approximations of…
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems
Luo Luo, Haishan Ye, Zhichao Huang +1
We consider nonconvex-concave minimax optimization problems of the form , where is strongly-concave in but p…