873 citations · 5.4k across the 120 of their papers we have counts for
6 papers · 1 filter
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu, Kaiqing Zhang, Tamer Başar +1
In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG (NPG) methods, and their variance-reduced variants, under general smooth policy parametriz…
Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization
Quan Xiao, Han Shen, Wotao Yin +1
Stochastic bilevel optimization, which captures the inherent nested structure of machine learning problems, is gaining popularity in many recent applications. Existing works on bil…
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years. Still, many classes of MILPs qu…
Communication-Efficient Topologies for Decentralized Learning with Consensus Rate
Zhuoqing Song, Weijian Li, Kexin Jin +4
Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server.…
On Representing Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the…
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
Xinmeng Huang, Yiming Chen, Wotao Yin +1
Recent advances in distributed optimization and learning have shown that communication compression is one of the most effective means of reducing communication. While there have be…