61 citations · 121 across the 3 of their papers we have counts for
4 papers
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…
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
Shuai Xiao, Junchi Yan, Stephen M. Chu +2
Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying…
Wasserstein Learning of Deep Generative Point Process Models
Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye +3
Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world pheno…
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
Shuai Xiao, Junchi Yan, Mehrdad Farajtabar +3
A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the event timestamps can carry impo…