no code implementations • 17 Aug 2022 • Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, Zhengyuan Zhu
Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question.
no code implementations • NeurIPS 2021 • Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu
To our knowledge, this paper is the first work that achieves both $\mathcal{O}(\epsilon^{-2})$ sample complexity and $\mathcal{O}(\epsilon^{-2})$ communication complexity in decentralized policy evaluation for cooperative MARL.
Multi-agent Reinforcement Learning Reinforcement Learning (RL) +1
no code implementations • 4 May 2021 • Xin Zhang, Jia Liu, Zhengyuan Zhu, Elizabeth S. Bentley
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity.
no code implementations • EACL 2021 • Zhengyuan Zhu, Kevin Meng, Josue Caraballo, Israa Jaradat, Xiao Shi, Zeyu Zhang, Farahnaz Akrami, Haojin Liao, Fatma Arslan, Damian Jimenez, Mohanmmed Samiul Saeef, Paras Pathak, Chengkai Li
This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter.
no code implementations • 12 Jan 2020 • Xin Zhang, Minghong Fang, Jia Liu, Zhengyuan Zhu
In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing.
no code implementations • 5 Jun 2019 • Xin Wang, Zhengyuan Zhu, Hao Helen Zhang
Spatial regression is widely used for modeling the relationship between a dependent variable and explanatory covariates.
Methodology
no code implementations • 28 May 2019 • Xin Zhang, Jia Liu, Zhengyuan Zhu
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network.
no code implementations • 23 Apr 2019 • Haozhe Zhang, Dan Nettleton, Zhengyuan Zhu
Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems.
no code implementations • 5 Apr 2019 • Xin Zhang, Zhengyuan Zhu
We develop theoretical properties of the method which indicates that asymptotically SCUSUM can reach high classification accuracy.
no code implementations • 24 May 2018 • Xin Zhang, Jia Liu, Zhengyuan Zhu
Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning.