no code implementations • 28 Mar 2024 • Zhiyuan Yao, Zheng Li, Matthew Thomas, Ionut Florescu
Investors and regulators can greatly benefit from a realistic market simulator that enables them to anticipate the consequences of their decisions in real markets.
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 • 1 Feb 2024 • Emanuele Mengoli, Zhiyuan Yao, Wutao Wei
To address this challenge, in this paper, we propose an end-to-end anomaly detection model development pipeline.
no code implementations • 1 Feb 2024 • Zhiyuan Yao, Ionut Florescu, Chihoon Lee
In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback.
no code implementations • 2 Jun 2023 • Yao Zhao, Sophine Zhang, Zhiyuan Yao
Anomaly detection is an important task in network management.
no code implementations • 25 Jun 2022 • Zhiyuan Yao, Tianyu Shi, Site Li, Yiting Xie, Yuanyuan Qin, Xiongjie Xie, Huan Lu, Yan Zhang
Axie infinity is a complicated card game with a huge-scale action space.
1 code implementation • 3 Jun 2022 • Zhiyuan Yao, Zihan Ding
A fully distributed MARL algorithm is proposed to approximate the Nash equilibrium of the game.
no code implementations • 27 Jan 2022 • Zhiyuan Yao, Zihan Ding, Thomas Clausen
This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 29 Oct 2021 • Zhiyuan Yao, Zihan Ding, Thomas Heide Clausen
Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services.
1 code implementation • 27 Oct 2021 • Zhiyuan Yao, Yoann Desmouceaux, Mark Townsley, Thomas Heide Clausen
This paper proposes Aquarius to bridge the gap between ML and networking systems and demonstrates its usage in the context of network load balancers.