Coauthor Index - Ask others: ACM DL/Guide - CiteSeerX - CSB - MetaPress - Google - Bing - Yahoo

* | 2009 | |
---|---|---|

51 | EE | Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul: Matrix updates for perceptron training of continuous density hidden Markov models. ICML 2009: 20 |

50 | EE | Youngmin Cho, Lawrence K. Saul: Learning dictionaries of stable autoregressive models for audio scene analysis. ICML 2009: 22 |

49 | EE | Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker: Identifying suspicious URLs: an application of large-scale online learning. ICML 2009: 86 |

48 | EE | Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker: Beyond blacklists: learning to detect malicious web sites from suspicious URLs. KDD 2009: 1245-1254 |

47 | EE | Stuart Russell, Lawrence K. Saul: Technical perspective - The ultimate pilot program. Commun. ACM 52(7): 96 (2009) |

2008 | ||

46 | EE | Kilian Q. Weinberger, Lawrence K. Saul: Fast solvers and efficient implementations for distance metric learning. ICML 2008: 1160-1167 |

2007 | ||

45 | EE | Fei Sha,
Y. Albert Park,
Lawrence K. Saul:
Multiplicative Updates for L_{1}-Regularized Linear and Logistic Regression.
IDA 2007: 13-24 |

44 | 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 | ||

43 | Kilian Q. Weinberger, Lawrence K. Saul: An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding. AAAI 2006 | |

42 | EE | Fei Sha, Lawrence K. Saul: Large Margin Hidden Markov Models for Automatic Speech Recognition. NIPS 2006: 1249-1256 |

41 | EE | Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul: Graph Laplacian Regularization for Large-Scale Semidefinite Programming. NIPS 2006: 1489-1496 |

40 | EE | Yun Mao, Lawrence K. Saul, Jonathan M. Smith: IDES: An Internet Distance Estimation Service for Large Networks. IEEE Journal on Selected Areas in Communications 24(12): 2273-2284 (2006) |

39 | 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 | ||

38 | EE | Fei Sha, Lawrence K. Saul: Analysis and extension of spectral methods for nonlinear dimensionality reduction. ICML 2005: 784-791 |

37 | EE | J. Ashley Burgoyne, Lawrence K. Saul: Learning Harmonic Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models. ISMIR 2005: 438-443 |

36 | EE | Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul: Distance Metric Learning for Large Margin Nearest Neighbor Classification. NIPS 2005 |

2004 | ||

35 | 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 | |

34 | EE | Kilian Q. Weinberger, Lawrence K. Saul: Unsupervised Learning of Image Manifolds by Semidefinite Programming. CVPR (2) 2004: 988-995 |

33 | EE | Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul: Learning a kernel matrix for nonlinear dimensionality reduction. ICML 2004 |

32 | EE | Yun Mao, Lawrence K. Saul: Modeling distances in large-scale networks by matrix factorization. Internet Measurement Conference 2004: 278-287 |

31 | EE | John Blitzer, Kilian Q. Weinberger, Lawrence K. Saul, Fernando Pereira: Hierarchical Distributed Representations for Statistical Language Modeling. NIPS 2004 |

30 | EE | Fei Sha, Lawrence K. Saul: Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. NIPS 2004 |

2003 | ||

29 | EE | Fei Sha, Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Large Margin Classifiers. COLT 2003: 188-202 |

28 | 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 | ||

27 | EE | Fei Sha, Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS 2002: 1041-1048 |

26 | 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 | ||

25 | EE | Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton: Global Coordination of Local Linear Models. NIPS 2001: 889-896 |

24 | EE | Lawrence K. Saul, Daniel D. Lee: Multiplicative Updates for Classification by Mixture Models. NIPS 2001: 897-904 |

23 | 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) |

22 | EE | Mazin G. Rahim, Giuseppe Riccardi, Lawrence K. Saul, Jeremy H. Wright, Bruce Buntschuh, Allen L. Gorin: Robust numeric recognition in spoken language dialogue. Speech Communication 34(1-2): 195-212 (2001) |

2000 | ||

21 | Lawrence K. Saul, Jont B. Allen: Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech. NIPS 2000: 807-813 | |

20 | Lawrence K. Saul, Mazin G. Rahim: Markov Processes on Curves. Machine Learning 41(3): 345-363 (2000) | |

19 | Lawrence K. Saul, Michael I. Jordan: Attractor Dynamics in Feedforward Neural Networks. Neural Computation 12(6): 1313-1335 (2000) | |

1999 | ||

18 | 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) | |

17 | 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 | ||

16 | Lawrence K. Saul: Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time. ICML 1998: 506-514 | |

15 | EE | Michael J. Kearns, Lawrence K. Saul: Inference in Multilayer Networks via Large Deviation Bounds. NIPS 1998: 260-266 |

14 | EE | Lawrence K. Saul, Mazin G. Rahim: Markov Processes on Curves for Automatic Speech Recognition. NIPS 1998: 751-760 |

13 | EE | Michael J. Kearns, Lawrence K. Saul: Large Deviation Methods for Approximate Probabilistic Inference. UAI 1998: 311-319 |

1997 | ||

12 | Lawrence K. Saul, Mazin G. Rahim: Modeling Acoustic Correlations by Factor Analysis. NIPS 1997 | |

11 | EE | Lawrence K. Saul, Fernando Pereira: Aggregate and mixed-order Markov models for statistical language processing CoRR cmp-lg/9706007: (1997) |

1996 | ||

10 | EE | Lawrence K. Saul, Satinder P. Singh: Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards. COLT 1996: 147-156 |

9 | EE | Lawrence K. Saul, Michael I. Jordan: A Variational Principle for Model-based Morphing. NIPS 1996: 267-273 |

8 | EE | Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul: Hidden Markov Decision Trees. NIPS 1996: 501-507 |

7 | EE | Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks CoRR cs.AI/9603102: (1996) |

6 | 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 | ||

5 | EE | Lawrence K. Saul, Satinder P. Singh: Markov Decision Processes in Large State Spaces. COLT 1995: 281-288 |

4 | EE | Lawrence K. Saul, Michael I. Jordan: Exploiting Tractable Substructures in Intractable Networks. NIPS 1995: 486-492 |

3 | EE | Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan: Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. NIPS 1995: 528-534 |

1994 | ||

2 | EE | Lawrence K. Saul, Michael I. Jordan: Boltzmann Chains and Hidden Markov Models. NIPS 1994: 435-442 |

1 | EE | Lawrence K. Saul, Michael I. Jordan: Learning in Boltzmann Trees. Neural Computation 6(6): 1174-1184 (1994) |