Y. Cho and L. K. Saul. Kernel methods for deep learning. Accepted to appear at NIPS-09; final version in preparation.
K. Q. Weinberger, J. Blitzer, and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. In NIPS-05. Oral presentation.
K. Q. Weinberger, F. Sha, and L. K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction. In ICML-04. Outstanding student paper award.
K. Q. Weinberger and L. K. Saul. Unsupervised learning of image manifolds by semidefinite programming. In CVPR-04. Outstanding student paper award.
S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science 290: 2323-2326. December 2000.
C.-C. Cheng, F. Sha, and L. K. Saul (2009). Large margin feature adaptation for automatic speech recognition. In ASRU-09.
F. Sha and L. K. Saul. Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models. In ICASSP-07. Student paper award finalist.
F. Sha and L. K. Saul. Large margin hidden Markov models for automatic speech recognition. In NIPS-06. Outstanding student paper award.
J. Ma, L. K. Saul, S. Savage, and G. M. Voelker. Identifying suspicious URLs: an application of large-scale online learning. In ICML-09.
Y. Mao, L. K. Saul, and J. M. Smith. IDES: An Internet distance estimation service for large networks. IEEE Journal On Selected Areas in Communications, 24:2273-2284. December 2006.
M. I. Jordan, Z. Ghahramani, T. Jaakkola, and L. K. Saul. An introduction to variational methods in graphical models. Machine Learning 37(2): 183-233. January 1999.
L. K. Saul, T. Jaakkola, and M. Jordan. Mean field theory for sigmoid belief networks. Journal of Artificial Intelligence Research 4:61-76. March 1996.