Fei Sha
Research Teaching Papers Talks Softwares

34. Leslie Cheng, Leana Golubchik, and Fei Sha. A study of web services performance prediction: a client's perspective. In Proceedings of the 19th Annual Meetings of the IEEE International Sym- posium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems (MASCOTS).   [ to appear ], Singapore, 2011
33. Junping Zhang, Ben Tan, Fei Sha and Li He. Predicting Pedestrian Counts in Crowded Scenes with Rich and High-dimensional Features. In IEEE Transactions on Intelligent Transportation Systems.   [ to appear ], 2011
32. Zhuoliang Kang, Kristen Grauman and Fei Sha. Learning with Whom to Share in Multi-task Feature Learning. In Proceedings of the International Conference on Machine Learning. Bellevue, WA., 2011.   [ PDF ] Supplementary material
31. Meihong Wang and Fei Sha. Information Theoretical Clustering via Semidefinite Programming. In Proceedings of the Artificial Intelligence and Statistics (AISTATS). Ft. Lauderdale, FL., 2011.   [ PDF ]
30. Sungju Hwang, Fei Sha and Kristen Grauman. Sharing Features between Objects and Their Attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Colorado Springs, CO., 2011.   [ PDF ]
29. Matthew E. Taylor, Brian Kullis, and Fei Sha. Metric Learning for Reinforcement Agents. In Proceedings of the Tenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS) Taipei, 2011.  
28. Meihong Wang, Fei Sha, and Michael I. Jordan. Unsupervised kernel dimension reduction. In Proceedings of Neural Information Processing Systems (NIPS) 2010 Vancouver, Canada 2010.   [ PDF ]
27. Sriram Sankararaman, Fei Sha, Kack F. Kirsch, Michael I. Jordan and Kimmen Sjolander. Active site predction using evolutionary and structural information. Bioinformatics vol. 26 (5), pages 617-624, 2010.   [ PDF ]
26. Dian Gong, Fei Sha, and Gerard Medioni. Locally linear denoising on image manifolds.In Proceedings of Artificial Intelligence and Statistics (AISTATS) 2010. Sardina, Italy 2010.   [ PDF ]
25. Chih-chieh Cheng, Fei Sha, and Lawrence K. Saul. Online learning and acoustic feature adaptation in large margin hidden Markov models. IEEE J. of Selected Topics in Signal Processing 4(6):926-942, 2010.   [ PDF ]
24. Chih-chieh Cheng, Fei Sha, and Lawrence K. Saul. Large margin feature adaptation for automatic speech recognition. Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU-09) Moreno, Italy, 2009.  
23. Chih-chieh Cheng, Fei Sha, and Lawrence K. Saul. A fast online algorithm for large margin training of conteinous-density hidden Markov models.In Proceedings of Tenth Annual Conference of International Speech Communication Association (Interspeech 09). Brighton, UK 2009.   [ PDF ]
22. Chih-chieh Cheng, Fei Sha, and Lawrence K. Saul. Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models.In Proceedings of Twenty Sixth International Conference on Machine Learning (ICML-09). Montreal, Canada 2009.   [ PDF ]
21. Nilesh N. Dalvi, Philip Bohannon and Fei Sha. Robust web extraction: an approach based on a probabilistic tree-edit model.In SIGMOD Conference 2009. Providence, RI 2009.   [ PDF ]
20. Simon Lacoste-Julien, Fei Sha, and Michael I. Jordan. DiscLDA: Discriminative learning for dimensionality reduction and classification.In Proceedings of Neural Information Processing Systems. Vancouver, CA 2008.   [ PDF ]
19. Fei Sha and Lawrence K. Saul. Large margin training of acoustic models for phoneme classification and recognition.Large Margin and Kernel Approaches to Speech and Speaker Recognition.J. Keshet and S. Bengio. Wiley & Sons, 2008.
18. Fei Sha, Yunanqing Lin, Lawrence K. Saul, and Daniel D. Lee. Multiplicative updates for nonnegative quadratic programming.Neural Computation, 19(8):2004-2031, 2007.  [ PDF ]
17. Fei Sha, Yonghahk Park, and Lawrence Saul.Multiplicative updates for L1- regularized linear and logistic regression.Advances in Intelligent Data Analysis VII: Proceedings of Seveth International Symposium on Intelligent Data Analysis (IDA 2007).Michael R. Berthold, John Shawe-Taylor, and Nada Lavrac. volume 4723 of Lecture Notes in Computer Science, pages 13-24. Ljubljana, Slovenia, 2007. Springer.  [ PDF ]
16. Kilian Q. Weinberger, Fei Sha, Qihui Zhu, and Lawrence K. Saul.Graph regularization for maximum variance unfolding, with an application to sensor localization. Advances in Neural Information Processing Systems 19, pages 1489-1496. B. Schölkopf, J.C. Platt, and T. Hofmann. Cambridge, MA, 2007. MIT Press.  [ PDF ]
15. Fei Sha.Large margin training of acoustic models for speech recognition.Ph.D Dissertation.University of Pennsylvania. 2007.  [ PDF ]
14. Andrea Frome, Yoram Singer, Fei Sha, and Jitendra Malik. Learning globally consistent local distance functions for shape-based image retrieval and classification.Proceedings of IEEE Eleventh International Conference on Computer Vision (ICCV 2007), pages 1-8.Rio de Janeiro, Brazil, 2007.  [ PDF ]
13. Jens Nilsson, Fei Sha, and Michael I. Jordan.Regression of data on manifold with kernel dimension reduction. Proceedings of the Twenty-Fourth Annual International Conference on Machine Learning(ICML 2007), pages 697-704. Z. Ghahramani. Corvallis,OR, 2007. MIT Press. [ PDF ]
12. Fei Sha and Lawrence K. Saul.Large margin hidden Markov models for automatic speech recognition Advances in Neural Information Processing Systems 19, pages 1249-1256. B. Schölkopf, J.C. Platt, and T. Hofmann.Cambridge, MA, 2007. MIT Press.  [ PDF ]  Outstanding Student Paper Award
11. Fei Sha and Lawrence K. Saul.Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), pages 313-316. Honolulu, HI, 2007.  [ PDF ]  Finalist of Best Student Paper Award
10. Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Hamm, and Daniel D. Lee. Spectral methods for dimensionality reduction. Semi-supervised Learning, pages 293-308. Olivier Chapelle, Bernhard Schöolkopf, and Alexander Zien. MIT Press, Cambridage, MA, 2006.
9. Fei Sha and Lawrence K. Saul.Large margin Gaussian mixture modeling for phonetic classification and recognition Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), pages 265-268. Toulouse, France, 2006.  [ PDF ]
8. Fei Sha and Lawrence K. Saul.Analysis and extension of spectral methods for nonlinear dimensionality reduction. Proceedings of the Twenty-second International Conference of Machine Learning (ICML 2005), pages 784-791. Bonn, Germany, 2005. ACM (New York, NY).  [ GZipped PDF ]
7. Fei Sha and Lawrence Saul. Real-time pitch determination of one or more voices by nonnegative matrix factorization. Advances in Neural Information Processing Systems 17, pages 1233-1240. L. K. Saul, Y. Weiss, and L. Bottou. MIT Press, Cambridge, MA, 2005.  [ PDF ]
6. Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction.Proceedings of the Twenty First International Conference on Machine Learning (ICML 2004), pages 839- 846, Banff, Canada, 2004.  [ PDF ]  Outstanding Student Paper Award
5. Fei Sha, J. Ashley Burgoyne, and Lawrence K. Saul. Multiband statistical learning for f0 estimation in speech. Proceedings of the IEEE International Conference of Acoustics, Speech and Signal Processing (ICASSP), volume 5, pages 661-664. Montreal, Canada, 2004.  [ PDF ]
4. Lawrence K. Saul, Fei Sha, and Daniel D. Lee. Statistical signal processing with nonnegativity constraints. Proceedings of the Eighth European Conference on Speech Communication and Technology(EuroSpeech 2003), pages 1001- 1004. Geneva, Switzerland, 2003.  [ PDF ]
3. Fei Sha, Lawrence K. Saul, and Daniel D. Lee.Multiplicative updates for large margin classifiers. Proceedings of the Sixteeth Annual Conference on Computational Learning Theory (COLT 2003).B. Schölkopf, M. Warmuth. volume 2777 of Lecture Notes in Artificial Intelligence, pages 188-202. , Washington D. C., 2003. Springer.  [ PDF ]
2. Fei Sha, Lawrence K. Saul, and Daniel D. Lee. Multiplicative updates for nonnegative quadratic programming in support vector machines. Advances in Neural and Information Processing Systems 15.S. Becker, S. Thrun, and K. Obermayer. Cambridge, MA, 2003. MIT Press.  [ PDF ]
1. Fei Sha and Fernando Pereira.Shallow Parsing with Conditional Random Fields.Proceedings of Human Language Technology-NAACL 2003, pages 213-220. Edmonton, Canada.  [ PDF ]
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