Wenlin Chen's Homepage
I am a third year Ph.D student in the Department of Computer Science and Engineering at Washington University in St. Louis. My advisor is Prof. Yixin Chen. I also work closely with Prof. Kilian Weinberger. Before joining WashU, I received my bachelor degree in computer science from University of Science and Technology of China (USTC) in 2011. During my undergraduate, I spent 6 months as an exchange student in National Taiwan University and 1 year as a research intern in Microsoft Research Area (MSRA).
Machine Learning, Data Mining, Artificial Intelligence
W. Chen, Y. Chen, Y. Mao, and B. Guo, Density-Based Logistic Regression, Proc. ACM SIGKDD Conference (KDD-13), 2013. (PDF)
W. Chen, K. Weinberger, and Y. Chen, Maximum Variance Correction with Application to A* Search, Proc. International Conference on Machine Learning (ICML-13), 2013. (PDF)
W. Chen, Y. Chen, K. Weinberger, Q. Lu, and X. Chen, Goal-Oriented Euclidean Heuristics with Manifold Learning, Proc. AAAI Conference on Artificial Intelligence (AAAI-13), 2013. (PDF)
Q. Lu, W. Chen, Y. Chen, K. Weinberger, and X. Chen, Utilizing Landmarks in Euclidean Heuristics for Optimal Planning, Late-Breaking Track, Proc. AAAI Conference on Artificial Intelligence (AAAI-13), 2013. (PDF)
Y. He, W. Chen, Y. Mao, and Y. Chen, Kernel Density Metric Learning, Proc. IEEE International Conference on Data Mining (ICDM-13), 2013. (PDF) Nomination for Best Paper Award
Y. Mao, W. Chen, Y. Chen, C. Lu, M. Kollef, and T. Bailey, An Integrated Data Mining Approach to Real-time Clinical Monitoring and Deterioration Warning, Proc. ACM SIGKDD Conference (KDD-12),2012. (PDF)
Program Committee: IJCAI 2013
Reviewer (Journal): TKDE, TIST, JAIR
Reviewer (Conference): ICML14, KDD13, ICDM13, CIKM13, KDD12, SDM12
Maximum Variance Correction finds large-scale feasible solutions to Maximum Variance Unfolding (MVU) by post-processing embeddings from any manifold learning algorithm. It increases the scale of MVU embeddings by several orders of magnitude and is naturally parallel.
Density-based Logistic Regression is a large-scale nonlinear classification method that supports for mix-type data, good interpretability and multinomial classification.
Email: wenlinchen AT wustl DOT edu
Office: Jolley Hall #535
Mailing Address: Campus Box 1045, One Brookings Drive, St. Louis, MO 63130