Wei Liu 刘威
Research Staff Member of IBM T. J. Watson Research Center
Ph.D. of Columbia University
Thesis supervisor: Professor Shih-Fu Chang
Email: weiliu at us.ibm.com or wliu at ee.columbia.edu
Research interests: machine learning, data mining, information retrieval, and
computer vision. My PhD thesis is dedicated to large-scale machine learning algorithms
for classification and search. Currently, I am working on combinatorial, parallel, and distributed optimization methods to tackle noise/outlier present, high-dimensional, and large-scale learning problems. I am particularly interested in developing binary coding
and hashing techniques to handle big data.
l I am teaching the Database Mining course at RPI in 2014 Fall.
l Two papers are accepted by NIPS’14, and one is spotlight oral.
l Two papers are accepted by CVPR’14 oral sessions.
l One paper is accepted by ECCV’14, and one paper is accepted by AAAI’14.
l Two papers are accepted by MICCAI’14.
l One paper wins the Best Paper Travel Award of IEEE International Symposium on Biomedical Imaging (ISBI), 2014.
l Three papers were accepted by ICCV’13. Thanks to the support of Josef Raviv Memorial Postdoctoral Fellowship.
l I act as a guest editor of Neurocomputing Special Issue on Learning for 3D Understanding 2013.
l I won Jury Award for best thesis of Department of Electrical Engineering, Columbia University, 2013.
l I passed my PhD defense on August 24th, 2012.
l I won Josef Raviv Memorial Postdoctoral Fellowship 2012-2013.
l I was awarded Facebook Fellowship 2011-2012 (one of five winners worldwide). Thanks to Facebook!
1. Large-Scale Machine Learning for Classification and Search Wei Liu. PhD Thesis 2012.
2. Robust and Scalable Graph-Based Semisupervised Learning Wei Liu et al. Proceedings of the IEEE 2012.
3. Video De-Fencing Yadong Mu, Wei Liu, and Shuicheng Yan. IEEE TCSVT 2014.
4. Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval and Clustering Steven C. H. Hoi, Wei Liu, and Shih-Fu Chang. TOMCCAP 2010
(a long version of CVPR 2008 with code therein).
5. Zeta Hull Pursuits: Learning Non-convex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang. NIPS 2014. New!
6. Discrete Graph Hashing Wei Liu et al. NIPS 2014 (spotlight oral). New!
7. Compact Hyperplane Hashing with Bilinear Functions Wei Liu et al. ICML 2012 (oral).
9. Large Graph Construction for Scalable Semi-Supervised Learning Wei Liu et al. ICML 2010 (oral). code
10. Semi-Supervised Sparse Metric Learning Using Alternating Linearization Optimization Wei Liu et al. KDD 2010 (full oral).
11. Unsupervised One-Class Learning for Automatic Outlier Removal Wei Liu et al. CVPR 2014 (oral). New!
13. Noise Resistant Graph Ranking for Improved Web Image Search Wei Liu et al. CVPR 2011.
14. Robust Multi-Class Transductive Learning with Graphs Wei Liu and Shih-Fu Chang. CVPR 2009. code
15. Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation Wei Liu et al. CVPR 2005 (oral).
16. Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval Steven C. H. Hoi, Wei Liu, and Shih-Fu Chang. CVPR 2008. code
17. A Face Annotation Framework with Partial Clustering and Interactive Labeling Yuandong Tian, Wei Liu, Rong Xiao, Fang Wen, and Xiaoou Tang. CVPR 2007.
18. Learning Distance Metrics with Contextual Constraints for Image Retrieval Steven C.H. Hoi, Wei Liu, Michael R. Lyu, and Wei-Ying Ma. CVPR 2006.
19. Nonparametric Subspace Analysis for Face Recognition Zhifeng Li, Wei Liu, Dahua Lin, and Xiaoou Tang. CVPR 2005.
20. Face Recognition via Archetype Hull Ranking Yuanjun Xiong, Wei Liu, Deli Zhao, and Xiaoou Tang. ICCV 2013.
21. Learning Hash Codes with Listwise Supervision Jun Wang, Wei Liu, Andy Sun, and Yu-Gang Jiang. ICCV 2013.
22. Output Regularized Metric Learning with Side Information Wei Liu et al. ECCV 2008.
23. Spatio-temporal Embedding for Statistical Face Recognition from Video Wei Liu et al. ECCV 2006.
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