3 citations · 8 across the 10 of their papers we have counts for
10 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…
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}…
Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation
Martin Figura, Yixuan Lin, Ji Liu +1
Adversarial attacks during training can strongly influence the performance of multi-agent reinforcement learning algorithms. It is, thus, highly desirable to augment existing algor…
Finite-Time Error Bounds for Distributed Linear Stochastic Approximation
Yixuan Lin, Vijay Gupta, Ji Liu
This paper considers a novel multi-agent linear stochastic approximation algorithm driven by Markovian noise and general consensus-type interaction, in which each agent evolves acc…
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
Yixuan Lin, Kaiqing Zhang, Zhuoran Yang +4
This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communica…