INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks

no code implementations • 27 Jul 2022 • Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu

Our main contributions in this paper are two-fold: i) We first propose a deterministic algorithm called INTERACT (inner-gradient-descent-outer-tracked-gradient) that requires the sample complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of $\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where $n$ and $\epsilon > 0$ are the number of samples at each agent and the desired stationarity gap, respectively.

Bilevel Optimization Meta-Learning +1

FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

no code implementations • 11 Jul 2022 • Luning Bi, Yunlong Wang, Fan Zhang, Zhuqing Liu, Yong Cai, Emily Zhao

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs).

Graph Attention Recommendation Systems

Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

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 +1

An empirical learning-based validation procedure for simulation workflow

no code implementations • 11 Sep 2018 • Zhuqing Liu, Liyuanjun Lai, Lin Zhang

Simulation workflow is a top-level model for the design and control of simulation process.