308 citations · 354 across the 7 of their papers we have counts for
6 papers · 1 filter
Off-Policy Imitation Learning from Observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai +1
Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit through the reuse of incomplete resources. Compared to conv…
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…
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai +1
Model-free deep reinforcement learning (RL) has demonstrated its superiority on many complex sequential decision-making problems. However, heavy dependence on dense rewards and hig…
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…
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…