2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
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}…