no code implementations • 11 Oct 2023 • Jingxuan Zhu, Alec Koppel, Alvaro Velasquez, Ji Liu
In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct stream of rewards, and seeks to exchange information with others to select a sequence of arms so as to minimize its regret.
no code implementations • 1 Apr 2023 • 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 arbitrarily introducing untrustworthy information in communication.
no code implementations • 26 Mar 2022 • Jingxuan Zhu, Yixuan Lin, Alvaro Velasquez, Ji Liu
This paper considers a resilient high-dimensional constrained consensus problem and studies a resilient distributed algorithm for complete graphs.
no code implementations • NeurIPS 2021 • Jingxuan Zhu, Ji Liu
This paper studies a homogeneous decentralized multi-armed bandit problem, in which a network of multiple agents faces the same set of arms, and each agent aims to minimize its own regret.
no code implementations • 24 Oct 2020 • Zhaowei Zhu, Jingxuan Zhu, Ji Liu, Yang Liu
Motivated by the proposal of federated learning, we aim for a solution with which agents will never share their local observations with a central entity, and will be allowed to only share a private copy of his/her own information with their neighbors.