61 citations · 129 across the 8 of their papers we have counts for
11 papers
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Weichang Wu, Junchi Yan, Xiaokang Yang +1
Temporal point process is an expressive tool for modeling event sequences over time. In this paper, we take a reinforcement learning view whereby the observed sequences are assumed…
Deep Spectral Clustering using Dual Autoencoder Network
Xu Yang, Cheng Deng, Feng Zheng +2
The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embeddi…
Decoupled Learning for Factorial Marked Temporal Point Processes
Weichang Wu, Junchi Yan, Xiaokang Yang +1
This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process m…
tau-FPL: Tolerance-Constrained Learning in Linear Time
Ao Zhang, Nan Li, Jian Pu +3
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge depe…
Joint Cuts and Matching of Partitions in One Graph
Tianshu Yu, Junchi Yan, Jieyi Zhao +1
As two fundamental problems, graph cuts and graph matching have been investigated over decades, resulting in vast literature in these two topics respectively. However the way of jo…
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…