Saul, Lawrence K.

60 publications

TMLR 2026 AC$\oplus$DC Search: Behind the Winning Solution to the FlyWire Graph-Matching Challenge Daniel Lee, Arie Matsliah, Lawrence K. Saul

AISTATS 2025 Batch, Match, and Patch: Low-Rank Approximations for Score-Based Variational Inference Chirag Modi, Diana Cai, Lawrence K. Saul

NeurIPS 2025 CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning Ningyuan Teresa Huang, Richard Stiskalek, Jun-Young Lee, Adrian E. Bayer, Charles Margossian, Christian Kragh Jespersen, Lucia A. Perez, Lawrence K. Saul, Francisco Villaescusa-Navarro

NeurIPS 2025 Fisher Meets Feynman: Score-Based Variational Inference with a Product of Experts Diana Cai, Robert M. Gower, David Blei, Lawrence K. Saul

JMLR 2025 Variational Inference for Uncertainty Quantification: An Analysis of Trade-Offs Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul

AISTATS 2025 Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix Charles Margossian, Lawrence K. Saul

ICML 2024 Batch and Match: Black-Box Variational Inference with a Score-Based Divergence Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David Blei, Lawrence K. Saul

NeurIPS 2024 EigenVI: Score-Based Variational Inference with Orthogonal Function Expansions Diana Cai, Chirag Modi, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul

ICMLW 2024 EigenVI: Score-Based Variational Inference with Orthogonal Function Expansions Diana Cai, Chirag Modi, Charles Margossian, Robert M. Gower, David Blei, Lawrence K. Saul

UAI 2023 The Shrinkage-Delinkage Trade-Off: An Analysis of Factorized Gaussian Approximations for Variational Inference Charles C. Margossian, Lawrence K. Saul

NeurIPS 2023 Variational Inference with Gaussian Score Matching Chirag Modi, Robert Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul

TMLR 2023 Weight-Balancing Fixes and Flows for Deep Learning Lawrence K. Saul

TMLR 2022 A Geometrical Connection Between Sparse and Low-Rank Matrices and Its Application to Manifold Learning Lawrence K. Saul

NeurIPS 2021 An Online Passive-Aggressive Algorithm for Difference-of-Squares Classification Lawrence K. Saul

AISTATS 2014 A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data Do-kyum Kim, Matthew F. Der, Lawrence K. Saul

NeurIPS 2012 Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der, Lawrence K. Saul

NeurIPS 2011 Maximum Covariance Unfolding : Manifold Learning for Bimodal Data Vijay Mahadevan, Chi W. Wong, Jose C. Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul

NeurIPS 2010 Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development Diane Hu, Laurens Maaten, Youngmin Cho, Sorin Lerner, Lawrence K. Saul

JMLR 2009 Distance Metric Learning for Large Margin Nearest Neighbor Classification Kilian Q. Weinberger, Lawrence K. Saul

ICML 2009 Identifying Suspicious URLs: An Application of Large-Scale Online Learning Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker

NeurIPS 2009 Kernel Methods for Deep Learning Youngmin Cho, Lawrence K. Saul

ICML 2009 Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis Youngmin Cho, Lawrence K. Saul

ICML 2009 Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul

ICML 2008 Fast Solvers and Efficient Implementations for Distance Metric Learning Kilian Q. Weinberger, Lawrence K. Saul

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

NeurIPS 2006 Graph Laplacian Regularization for Large-Scale Semidefinite Programming Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul

NeurIPS 2006 Large Margin Hidden Markov Models for Automatic Speech Recognition Fei Sha, Lawrence K. Saul

ICML 2005 Analysis and Extension of Spectral Methods for Nonlinear Dimensionality Reduction Fei Sha, Lawrence K. Saul

NeurIPS 2005 Distance Metric Learning for Large Margin Nearest Neighbor Classification Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul

NeurIPS 2004 Hierarchical Distributed Representations for Statistical Language Modeling John Blitzer, Fernando Pereira, Kilian Q. Weinberger, Lawrence K. Saul

ICML 2004 Learning a Kernel Matrix for Nonlinear Dimensionality Reduction Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul

NeurIPS 2004 Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization Fei Sha, Lawrence K. Saul

CVPR 2004 Unsupervised Learning of Image Manifolds by Semidefinite Programming Kilian Q. Weinberger, Lawrence K. Saul

AISTATS 2003 A Generalized Linear Model for Principal Component Analysis of Binary Data Andrew I. Schein, Lawrence K. Saul, Lyle H. Ungar

COLT 2003 Multiplicative Updates for Large Margin Classifiers Fei Sha, Lawrence K. Saul, Daniel D. Lee

JMLR 2003 Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds Lawrence K. Saul, Sam T. Roweis

NeurIPS 2002 Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines Fei Sha, Lawrence K. Saul, Daniel D. Lee

NeurIPS 2002 Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch Lawrence K. Saul, Daniel D. Lee, Charles L. Isbell, Yann L. Cun

NeurIPS 2001 Global Coordination of Local Linear Models Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton

NeurIPS 2001 Multiplicative Updates for Classification by Mixture Models Lawrence K. Saul, Daniel D. Lee

NeCo 2000 Attractor Dynamics in Feedforward Neural Networks Lawrence K. Saul, Michael I. Jordan

MLJ 2000 Markov Processes on Curves Lawrence K. Saul, Mazin G. Rahim

NeurIPS 2000 Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech Lawrence K. Saul, Jont B. Allen

MLJ 1999 An Introduction to Variational Methods for Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul

MLJ 1999 Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones Lawrence K. Saul, Michael I. Jordan

ICML 1998 Automatic Segmentation of Continuous Trajectories with Invariance to Nonlinear Warpings of Time Lawrence K. Saul

NeurIPS 1998 Inference in Multilayer Networks via Large Deviation Bounds Michael J. Kearns, Lawrence K. Saul

UAI 1998 Large Deviation Methods for Approximate Probabilistic Inference Michael J. Kearns, Lawrence K. Saul

NeurIPS 1998 Markov Processes on Curves for Automatic Speech Recognition Lawrence K. Saul, Mazin G. Rahim

AISTATS 1997 Mixed Memory Markov Models Lawrence K. Saul, Michael I. Jordan

NeurIPS 1997 Modeling Acoustic Correlations by Factor Analysis Lawrence K. Saul, Mazin G. Rahim

NeurIPS 1996 A Variational Principle for Model-Based Morphing Lawrence K. Saul, Michael I. Jordan

NeurIPS 1996 Hidden Markov Decision Trees Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul

COLT 1996 Learning Curve Bounds for a Markov Decision Process with Undiscounted Rewards Lawrence K. Saul, Satinder P. Singh

JAIR 1996 Mean Field Theory for Sigmoid Belief Networks Lawrence K. Saul, Tommi S. Jaakkola, Michael I. Jordan

NeurIPS 1995 Exploiting Tractable Substructures in Intractable Networks Lawrence K. Saul, Michael I. Jordan

NeurIPS 1995 Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan

COLT 1995 Markov Decision Processes in Large State Spaces Lawrence K. Saul, Satinder P. Singh

NeurIPS 1994 Boltzmann Chains and Hidden Markov Models Lawrence K. Saul, Michael I. Jordan

NeCo 1994 Learning in Boltzmann Trees Lawrence K. Saul, Michael I. Jordan