308 citations · 396 across the 11 of their papers we have counts for
6 papers · 1 filter
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning
Tongzhou Mu, Kaixiang Lin, Feiyang Niu +1
We present a two-step hybrid reinforcement learning (RL) policy that is designed to generate interpretable and robust hierarchical policies on the RL problem with graph-based input…
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg
Zhaonan Qu, Kaixiang Lin, Zhaojian Li +2
Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other's privately held data. The characteristics of non-i.i.d. data across t…
Ranking Policy Gradient
Kaixiang Lin, Jiayu Zhou
Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art estimates the optimal action values while it usually involves an extensive search…
Differentially Private Generative Adversarial Network
Liyang Xie, Kaixiang Lin, Shu Wang +2
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical perfo…
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation
Fengyi Tang, Kaixiang Lin, Ikechukwu Uchendu +2
Mild cognitive impairment (MCI) is a prodromal phase in the progression from normal aging to dementia, especially Alzheimers disease. Even though there is mild cognitive decline in…
Collaborative Deep Reinforcement Learning
Kaixiang Lin, Shu Wang, Jiayu Zhou
Besides independent learning, human learning process is highly improved by summarizing what has been learned, communicating it with peers, and subsequently fusing knowledge from di…