My PhD research focuses on statistical machine learning and variational Bayesian Learning. Specifically, I develop efficient statistical Bayesian models for pattern clustering and patter recognition tasks and I investigate the associate underlying optimization mechanism which drives the model towards successful inference and regression. My research also pays emphasis on sparsity driven machine learning phenomenon. My resent research successfully derives the closed form sparsity driven cost function hidden behind the hierarchical statistical clustering techniques (ICML 2015).
Generally, my research interests include Machine Learning, Bayesian Learning, Sparsity Learning, Learning theory, Optimization, Duality Analysis. More specifically, I am interested in applying efficient learning algorithms to the problem of clustering, subspace segmentation, pattern recognition.
1. Wei Chen, David Wipf, Yu Wang, Yang Liu, Ian Wassell, "Simultaneous Bayesian Sparse Approximation With Structured Sparse Models," IEEE Transactions on Signal Processing, Dec 2016
2. Yu Wang, David Wipf, Jeong Min Yun, Wei Chen, Ian J. Wassell, "Clustered Sparse Bayesian Learning," The Conference on Uncertainty in Artificial Intelligence (UAI), Jul 2015
3. Yu Wang, David Wipf, Qing Ling, Wei Chen, Ian Wassell, "Multi-Task Learning for Subspace Segmentation," International Conference on Machine Learning (ICML), Jul 2015
4. Yu Wang, David Wipf, Wei Chen, Ian Wassell, "Exploiting the Convex-Concave Penalty for Tracking: A Novel Dynamic Reweighted Sparse Bayesian Learning Algorithm," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014
5. Yu Wang, Wei Chen, Ian Wassell, "Exploiting Hidden Block Sparsity: Interdependent Matching Pursuit for Cyclic Feature Detection," IEEE Global Communications Conference (GLOBECOM), Dec 2013