Lawrence K. Saul

2008
43	EE	Kilian Q. Weinberger, Lawrence K. Saul: Fast solvers and efficient implementations for distance metric learning. ICML 2008: 1160-1167
2007
42	EE	Fei Sha, Y. Albert Park, Lawrence K. Saul: Multiplicative Updates for L1-Regularized Linear and Logistic Regression. IDA 2007: 13-24
41	EE	Fei Sha, Yuanqing Lin, Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Nonnegative Quadratic Programming. Neural Computation 19(8): 2004-2031 (2007)
2006
40		Kilian Q. Weinberger, Lawrence K. Saul: An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding. AAAI 2006
39	EE	Fei Sha, Lawrence K. Saul: Large Margin Hidden Markov Models for Automatic Speech Recognition. NIPS 2006: 1249-1256
38	EE	Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul: Graph Laplacian Regularization for Large-Scale Semidefinite Programming. NIPS 2006: 1489-1496
37	EE	Kilian Q. Weinberger, Lawrence K. Saul: Unsupervised Learning of Image Manifolds by Semidefinite Programming. International Journal of Computer Vision 70(1): 77-90 (2006)
2005
36	EE	Fei Sha, Lawrence K. Saul: Analysis and extension of spectral methods for nonlinear dimensionality reduction. ICML 2005: 784-791
35	EE	J. Ashley Burgoyne, Lawrence K. Saul: Learning Harmonic Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models. ISMIR 2005: 438-443
34	EE	Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul: Distance Metric Learning for Large Margin Nearest Neighbor Classification. NIPS 2005
2004
33		Sebastian Thrun, Lawrence K. Saul, Bernhard Schölkopf: Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, NIPS 2003, December 8-13, 2003, Vancouver and Whistler, British Columbia, Canada] MIT Press 2004
32	EE	Kilian Q. Weinberger, Lawrence K. Saul: Unsupervised Learning of Image Manifolds by Semidefinite Programming. CVPR (2) 2004: 988-995
31	EE	Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul: Learning a kernel matrix for nonlinear dimensionality reduction. ICML 2004
30	EE	Yun Mao, Lawrence K. Saul: Modeling distances in large-scale networks by matrix factorization. Internet Measurement Conference 2004: 278-287
29	EE	John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, Fernando Pereira: Hierarchical Distributed Representations for Statistical Language Modeling. NIPS 2004
28	EE	Fei Sha, Lawrence K. Saul: Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. NIPS 2004
2003
27	EE	Fei Sha, Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Large Margin Classifiers. COLT 2003: 188-202
26	EE	Lawrence K. Saul, Sam T. Roweis: Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold. Journal of Machine Learning Research 4: 119-155 (2003)
2002
25	EE	Fei Sha, Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS 2002: 1041-1048
24	EE	Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, Yann LeCun: Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch. NIPS 2002: 1181-1188
2001
23	EE	Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton: Global Coordination of Local Linear Models. NIPS 2001: 889-896
22	EE	Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Classification by Mixture Models. NIPS 2001: 897-904
21	EE	Lawrence K. Saul, Mazin G. Rahim, Jont B. Allen: A statistical model for robust integration of narrowband cues in speech. Computer Speech & Language 15(2): 175-194 (2001)
2000
20		Lawrence K. Saul, Jont B. Allen: Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech. NIPS 2000: 807-813
19		Lawrence K. Saul, Mazin G. Rahim: Markov Processes on Curves. Machine Learning 41(3): 345-363 (2000)
18		Lawrence K. Saul, Michael I. Jordan: Attractor Dynamics in Feedforward Neural Networks. Neural Computation 12(6): 1313-1335 (2000)
1999
17		Lawrence K. Saul, Michael I. Jordan: Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Machine Learning 37(1): 75-87 (1999)
16		Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola, Lawrence K. Saul: An Introduction to Variational Methods for Graphical Models. Machine Learning 37(2): 183-233 (1999)
1998
15		Lawrence K. Saul: Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time. ICML 1998: 506-514
14	EE	Michael J. Kearns, Lawrence K. Saul: Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266
13	EE	Lawrence K. Saul, Mazin G. Rahim: Markov Processes on Curves for Automatic Speech Recognition. NIPS 1998: 751-760
12	EE	Michael J. Kearns, Lawrence K. Saul: Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319
1997
11		Lawrence K. Saul, Mazin G. Rahim: Modeling Acoustic Correlations by Factor Analysis. NIPS 1997
10	EE	Lawrence K. Saul, Fernando Pereira: Aggregate and mixed-order Markov models for statistical language processing CoRR cmp-lg/9706007: (1997)
1996
9	EE	Lawrence K. Saul, Satinder P. Singh: Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. COLT 1996: 147-156
8	EE	Lawrence K. Saul, Michael I. Jordan: A Variational Principle for Model-based Morphing. NIPS 1996: 267-273
7	EE	Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul: Hidden Markov Decision Trees. NIPS 1996: 501-507
6	EE	Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks CoRR cs.AI/9603102: (1996)
5		Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. J. Artif. Intell. Res. (JAIR) 4: 61-76 (1996)
1995
4	EE	Lawrence K. Saul, Satinder P. Singh: Markov Decision Processes in Large State Spaces. COLT 1995: 281-288
3	EE	Lawrence K. Saul, Michael I. Jordan: Exploiting Tractable Substructures in Intractable Networks. NIPS 1995: 486-492
2	EE	Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534
1994
1	EE	Lawrence K. Saul, Michael I. Jordan: Boltzmann Chains and Hidden Markov Models. NIPS 1994: 435-442

Coauthor Index

1	Jont B. Allen	[20] [21]
2	John Blitzer	[29] [34]
3	J. Ashley Burgoyne	[35]
4	Zoubin Ghahramani	[7] [16]
5	Geoffrey E. Hinton	[23]
6	Charles Lee Isbell Jr. (Charles L. Isbell)	[24]
7	Tommi Jaakkola	[2] [5] [6] [16]
8	Michael I. Jordan	[1] [2] [3] [5] [6] [7] [8] [16] [17] [18]
9	Michael J. Kearns	[12] [14]
10	Yann LeCun	[24]
11	Daniel D. Lee	[22] [24] [25] [27] [41]
12	Yuanqing Lin	[41]
13	Yun Mao	[30]
14	Y. Albert Park	[42]
15	Fernando Pereira	[10] [29]
16	Mazin G. Rahim	[11] [13] [19] [21]
17	Sam T. Roweis	[23] [26]
18	Bernhard Schölkopf	[33]
19	Fei Sha	[25] [27] [28] [31] [36] [38] [39] [41] [42]
20	Satinder P. Singh	[4] [9]
21	Sebastian Thrun	[33]
22	Kilian Q. Weinberger	[29] [31] [32] [34] [37] [38] [40] [43]
23	Qihui Zhu	[38]

Colors in the list of coauthors