2 code implementations • 20 Feb 2024 • Qianqian Xie, Weiguang Han, Zhengyu Chen, Ruoyu Xiang, Xiao Zhang, Yueru He, Mengxi Xiao, Dong Li, Yongfu Dai, Duanyu Feng, Yijing Xu, Haoqiang Kang, Ziyan Kuang, Chenhan Yuan, Kailai Yang, Zheheng Luo, Tianlin Zhang, Zhiwei Liu, Guojun Xiong, Zhiyang Deng, Yuechen Jiang, Zhiyuan Yao, Haohang Li, Yangyang Yu, Gang Hu, Jiajia Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, Benyou Wang, Yanzhao Lai, Hao Wang, Min Peng, Sophia Ananiadou, Jimin Huang
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
no code implementations • 17 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.
no code implementations • 16 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.
no code implementations • 11 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.
no code implementations • 12 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.
no code implementations • 26 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.
no code implementations • 20 Sep 2021 • Guojun Xiong, Jian Li, Rahul Singh
We call it the R(MA)^2B-UCB algorithm.
no code implementations • 11 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.
no code implementations • 10 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.