J
Jiangchuan Zheng
Researcher at Hong Kong University of Science and Technology
Publications - 10
Citations - 354
Jiangchuan Zheng is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Collaborative filtering & Population. The author has an hindex of 9, co-authored 10 publications receiving 307 citations.
Papers
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Proceedings Article
Time-dependent trajectory regression on road networks via multi-task learning
Jiangchuan Zheng,Lionel M. Ni +1 more
TL;DR: This paper unify multiple different trajectory regression problems in a multi-task framework by introducing a novel crosstask regularization which encourages temporal smoothness on the change of road travel costs and proposes an efficient block coordinate descent method to solve the resulting problem.
Proceedings ArticleDOI
An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data
Jiangchuan Zheng,Lionel M. Ni +1 more
TL;DR: A probabilistic framework which can automatically learn characteristic behavior patterns in users' daily lives from mass amount of mobile data in unsupervised setting and exploit it to predict user activities is proposed.
Proceedings Article
Robust Bayesian inverse reinforcement learning with sparse behavior noise
TL;DR: This paper develops a robust IRL framework that can accurately estimate the reward function in the presence of behavior noise, and introduces a novel latent variable characterizing the reliability of each expert action and uses Laplace distribution as its prior.
Proceedings ArticleDOI
Modelling heterogeneous location habits in human populations for location prediction under data sparsity
TL;DR: A Bayesian model of mobility in populations (i.e., groups without spatial or social interconnections) that intelligently shares temporal parameters between people, but keeps the spatial parameters specific to individuals is presented.
Proceedings ArticleDOI
Effective routine behavior pattern discovery from sparse mobile phone data via collaborative filtering
TL;DR: Although a single user's mobile data is far from sufficient to reveal his characteristic behavior, it is shown that when exploiting a large number of users' mobile data in a principled collaborative way which facilitate similar users' data to complement each other, representative routine patterns can be revealed and each user can be characterized properly.