10 citations · 24 across the 17 of their papers we have counts for
11 papers · 1 filter
On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations
Guojun Xiong, Shufan Wang, Daniel Jiang +1
Federated reinforcement learning (FedRL) enables multiple agents to collaboratively learn a policy without sharing their local trajectories collected during agent-environment inter…
DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback
Guojun Xiong, Ujwal Dinesha, Debajoy Mukherjee +2
Restless multi-armed bandits (RMAB) has been widely used to model constrained sequential decision making problems, where the state of each restless arm evolves according to a Marko…
Decentralized Federated Learning with Model Caching on Mobile Agents
Xiaoyu Wang, Guojun Xiong, Houwei Cao +2
Federated Learning (FL) trains a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model ex…
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
Guojun Xiong, Jian Li
Restless multi-armed bandits (RMAB) play a central role in modeling sequential decision making problems under an instantaneous activation constraint that at most B arms can be acti…
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints
Shufan Wang, Guojun Xiong, Jian Li
Restless multi-armed bandits (RMAB) have been widely used to model sequential decision making problems with constraints. The decision maker (DM) aims to maximize the expected total…
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations
Guojun Xiong, Gang Yan, Shiqiang Wang +1
Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalabi…
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation
Guojun Xiong, Jian Li
Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB) problem. Although it is provably asymptotically optimal, finding Whittle indices remains…
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
Guojun Xiong, Gang Yan, Shiqiang Wang +1
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular…
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits
Guojun Xiong, Jian Li
Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by applications to cognitive radio systems. Most research for this problem focuses ex…
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits
Guojun Xiong, Jian Li, Rahul Singh
We study a finite-horizon restless multi-armed bandit problem with multiple actions, dubbed R(MA)^2B. The state of each arm evolves according to a controlled Markov decision proces…
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
Guojun Xiong, Gang Yan, Rahul Singh +1
With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the…