3 citations · 6 across the 4 of their papers we have counts for
9 papers
No-Regret Learning in Network Stochastic Zero-Sum Games
Shijie Huang, Jinlong Lei, Yiguang Hong
No-regret learning has been widely used to compute a Nash equilibrium in two-person zero-sum games. However, there is still a lack of regret analysis for network stochastic zero-su…
Distributed coordination for seeking the optimal Nash equilibrium of aggregative games
Xiaoyu Ma, Jinlong Lei, Peng Yi +1
This paper aims to design a distributed coordination algorithm for solving a multi-agent decision problem with a hierarchical structure. The primary goal is to search the Nash equi…
No-regret learning for repeated non-cooperative games with lossy bandits
Wenting Liu, Jinlong Lei, Peng Yi +1
This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give the explicit model of the utility functions i…
No-regret distributed learning in subnetwork zero-sum games
Shijie Huang, Jinlong Lei, Yiguang Hong +2
In this paper, we consider a distributed learning problem in a subnetwork zero-sum game, where agents are competing in different subnetworks. These agents are connected through tim…
Linearly Convergent Algorithm with Variance Reduction for Distributed Stochastic Optimization
Jinlong Lei, Peng Yi, Jie Chen +1
This paper considers a distributed stochastic strongly convex optimization, where agents connected over a network aim to cooperatively minimize the average of all agents' local cos…
Distributed Variable Sample-Size Gradient-response and Best-response Schemes for Stochastic Nash Equilibrium Problems over Graphs
Jinlong Lei, Uday V. Shanbhag
This paper considers a stochastic Nash game in which each player minimizes an expectation valued composite objective. We make the following contributions. (I) Under suitable monoto…