113 citations · 385 across the 19 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
cs.LG2018★ 96 cited
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li +3
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither…
cs.LG2018★ 50 cited
Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu +3
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time. The generative processes of these event data can be ver…
cs.LG2018★ 17 cited
Learning More Robust Features with Adversarial Training
Shuangtao Li, Yuanke Chen, Yanlin Peng +1
In recent years, it has been found that neural networks can be easily fooled by adversarial examples, which is a potential safety hazard in some safety-critical applications. Many…