1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.ML2020
Semi-supervised deep learning for high-dimensional uncertainty quantification
Zequn Wang, Mingyang Li
Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. This paper presents a sem…
cs.LG2019
Proximal Reliability Optimization for Reinforcement Learning
Narendra Patwardhan, Zequn Wang
Despite the numerous advances, reinforcement learning remains away from widespread acceptance for autonomous controller design as compared to classical methods due to lack of abili…
cs.LG2019★ 1 cited
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction
Narendra Patwardhan, Zequn Wang
Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate…