2 citations · 3 across the 9 of their papers we have counts for
9 papers
An Analysis Tool for Push-Sum Based Distributed Optimization
Yixuan Lin, Ji Liu
The push-sum algorithm is probably the most important distributed averaging approach over directed graphs, which has been applied to various problems including distributed optimiza…
A Resilient Distributed Algorithm for Solving Linear Equations
Jingxuan Zhu, Alvaro Velasquez, Ji Liu
This paper presents a resilient distributed algorithm for solving a system of linear algebraic equations over a multi-agent network in the presence of Byzantine agents capable of a…
Heterogeneous Distributed Subgradient
Yixuan Lin, Ji Liu
The paper proposes a heterogeneous push-sum based subgradient algorithm for multi-agent distributed convex optimization in which each agent can arbitrarily switch between subgradie…
Resilient Distributed Optimization
Jingxuan Zhu, Yixuan Lin, Alvaro Velasquez +1
This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resil…
Cooperative Actor-Critic via TD Error Aggregation
Martin Figura, Yixuan Lin, Ji Liu +1
In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function…
Reaching a Consensus with Limited Information
Jingxuan Zhu, Yixuan Lin, Ji Liu +1
In its simplest form the well known consensus problem for a networked family of autonomous agents is to devise a set of protocols or update rules, one for each agent, which can ena…
Resilient Constrained Consensus over Complete Graphs via Feasibility Redundancy
Jingxuan Zhu, Yixuan Lin, Alvaro Velasquez +1
This paper considers a resilient high-dimensional constrained consensus problem and studies a resilient distributed algorithm for complete graphs. For convex constrained sets with…
Subgradient-Push Is of the Optimal Convergence Rate
Yixuan Lin, Ji Liu
The push-sum based subgradient is an important method for distributed convex optimization over unbalanced directed graphs, which is known to converge at a rate of $O(\ln t/\sqrt{t}…
Reinforcement Learning based Distributed Control of Dissipative Networked Systems
K. C. Kosaraju, S. Sivaranjani, W. Suttle +2
We consider the problem of designing distributed controllers to stabilize a class of networked systems, where each subsystem is dissipative and designs a reinforcement learning bas…