Search Results for author: Guojun Xiong

Found 9 papers, 1 papers with code

DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations

no code implementations17 Dec 2023 Guojun Xiong, Gang Yan, Shiqiang Wang, Jian Li

Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalability issues due to the need of a central server.

Learning Theory Representation Learning

Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints

no code implementations16 Dec 2023 Shufan Wang, Guojun Xiong, Jian Li

Restless multi-armed bandits (RMAB) have been widely used to model sequential decision making problems with constraints.

Decision Making Fairness +2

Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates

no code implementations11 Jun 2023 Guojun Xiong, Gang Yan, Shiqiang Wang, Jian Li

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 parameter server framework.

Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits

no code implementations12 Dec 2022 Guojun Xiong, Jian Li

Most research for this problem focuses exclusively on the settings that players have \textit{full access} to all arms and receive no reward when pulling the same arm.

Decision Making Distributed Optimization

Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation

no code implementations26 Feb 2022 Guojun Xiong, Shufan Wang, Jian Li, Rahul Singh

Using this structural result, we establish the indexability of our problem, and employ the Whittle index policy to minimize average latency.

Edge-computing Q-Learning +1

Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers

no code implementations11 Feb 2021 Guojun Xiong, Gang Yan, Rahul Singh, Jian Li

In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updates it by waiting and averaging all estimates obtained from its neighbors, and then corrects it on the basis of its local dataset.

BIG-bench Machine Learning Distributed Optimization

Learning Augmented Index Policy for Optimal Service Placement at the Network Edge

no code implementations10 Jan 2021 Guojun Xiong, Rahul Singh, Jian Li

We pose the problem as a Markov decision process (MDP) in which the system state is given by describing, for each service, the number of customers that are currently waiting at the edge to obtain the service.

Q-Learning

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