Boltzmann Chains and Hidden Markov Models. - Lawrence K. Saul; Michael I. Jordan (NIPS, 1994)

Learning in Boltzmann Trees. - Lawrence K. Saul; Michael I. Jordan (Neural Comput., 1994)

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

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. - Tommi S. Jaakkola; Lawrence K. Saul; Michael I. Jordan (NIPS, 1995)

Exploiting Tractable Substructures in Intractable Networks. - Lawrence K. Saul; Michael I. Jordan (NIPS, 1995)

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

Hidden Markov Decision Trees. - Michael I. Jordan; Zoubin Ghahramani; Lawrence K. Saul (NIPS, 1996)

A Variational Principle for Model-based Morphing. - Lawrence K. Saul; Michael I. Jordan (NIPS, 1996)

Mean Field Theory for Sigmoid Belief Networks. - Lawrence K. Saul; Tommi S. Jaakkola; Michael I. Jordan (J. Artif. Intell. Res., 1996)

Mixed Memory Markov Models. - Lawrence K. Saul; Michael I. Jordan (AISTATS, 1997)

Aggregate and mixed-order Markov models for statistical language processing. - Lawrence K. Saul; Fernando Pereira 003 (EMNLP, 1997)

Modeling Acoustic Correlations by Factor Analysis. - Lawrence K. Saul; Mazin G. Rahim (NIPS, 1997)

An Introduction to Variational Methods for Graphical Models. - Michael I. Jordan; Zoubin Ghahramani; Tommi S. Jaakkola; Lawrence K. Saul (Learning in Graphical Models, 1998)

A Mean Field Learning Algorithm for Unsupervised Neural Networks. - Lawrence K. Saul; Michael I. Jordan (Learning in Graphical Models, 1998)

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

Inference in Multilayer Networks via Large Deviation Bounds. - Michael J. Kearns; Lawrence K. Saul (NIPS, 1998)

Markov Processes on Curves for Automatic Speech Recognition. - Lawrence K. Saul; Mazin G. Rahim (NIPS, 1998)

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

Modeling the rate of speech by Markov processes on curves. - Lawrence K. Saul; Mazin G. Rahim (EUROSPEECH, 1999)

An Introduction to Variational Methods for Graphical Models. - Michael I. Jordan; Zoubin Ghahramani; Tommi S. Jaakkola; Lawrence K. Saul (Mach. Learn., 1999)

Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. - Lawrence K. Saul; Michael I. Jordan (Mach. Learn., 1999)

Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech. - Lawrence K. Saul; Jont B. Allen (NIPS, 2000)

Markov Processes on Curves. - Lawrence K. Saul; Mazin G. Rahim (Mach. Learn., 2000)

Attractor Dynamics in Feedforward Neural Networks. - Lawrence K. Saul; Michael I. Jordan (Neural Comput., 2000)

Maximum likelihood and minimum classification error factor analysis for automatic speech recognition. - Lawrence K. Saul; Mazin G. Rahim (IEEE Trans. Speech Audio Process., 2000)

Global Coordination of Local Linear Models. - Sam T. Roweis; Lawrence K. Saul; Geoffrey E. Hinton (NIPS, 2001)

Multiplicative Updates for Classification by Mixture Models. - Lawrence K. Saul; Daniel D. Lee (NIPS, 2001)

A statistical model for robust integration of narrowband cues in speech. - Lawrence K. Saul; Mazin G. Rahim; Jont B. Allen (Comput. Speech Lang., 2001)

Robust numeric recognition in spoken language dialogue. - Mazin G. Rahim; Giuseppe Riccardi; Lawrence K. Saul; Jeremy H. Wright; Bruce Buntschuh; Allen L. Gorin (Speech Commun., 2001)

Real Time Voice Processing with Audiovisual Feedback: Toward Autonomous Agents with Perfect Pitch. - Lawrence K. Saul; Daniel D. Lee; Charles L. Isbell Jr.; Yann LeCun (NIPS, 2002)

Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. - Fei Sha; Lawrence K. Saul; Daniel D. Lee (NIPS, 2002)

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

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

Statistical signal processing with nonnegativity constraints. - Lawrence K. Saul; Fei Sha; Daniel D. Lee (INTERSPEECH, 2003)

Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold. - Lawrence K. Saul; Sam T. Roweis (J. Mach. Learn. Res., 2003)

Exploratory analysis and visualization of speech and music by locally linear embedding. - Viren Jain; Lawrence K. Saul (ICASSP (3), 2004)

Nonnegative deconvolution for time of arrival estimation. - Yuanqing Lin; Daniel D. Lee; Lawrence K. Saul (ICASSP (2), 2004)

Multiband statistical learning for f0 estimation in speech. - Fei Sha; John Ashley Burgoyne; Lawrence K. Saul (ICASSP (5), 2004)

Learning a kernel matrix for nonlinear dimensionality reduction. - Kilian Q. Weinberger; Fei Sha; Lawrence K. Saul (ICML, 2004)

Modeling distances in large-scale networks by matrix factorization. - Yun Mao; Lawrence K. Saul (Internet Measurement Conference, 2004)

Hierarchical Distributed Representations for Statistical Language Modeling. - John Blitzer; Kilian Q. Weinberger; Lawrence K. Saul; Fernando Pereira 003 (NIPS, 2004)

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

Semisupervised alignment of manifolds. - Jihun Ham; Daniel D. Lee; Lawrence K. Saul (AISTATS, 2005)

Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization. - Kilian Q. Weinberger; Benjamin Packer; Lawrence K. Saul (AISTATS, 2005)

Visualization of low Dimensional Structure in tonal pitch Space. - John Ashley Burgoyne; Lawrence K. Saul (ICMC, 2005)

Analysis and extension of spectral methods for nonlinear dimensionality reduction. - Fei Sha; Lawrence K. Saul (ICML, 2005)

Learning Harmonic Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models. - John Ashley Burgoyne; Lawrence K. Saul (ISMIR, 2005)

Distance Metric Learning for Large Margin Nearest Neighbor Classification. - Kilian Q. Weinberger; John Blitzer; Lawrence K. Saul (NIPS, 2005)

Spectral Methods for Dimensionality Reduction. - Lawrence K. Saul; Kilian Q. Weinberger; Fei Sha; Jihun Ham; Daniel D. Lee (Semi-Supervised Learning, 2006)

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

Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition. - Fei Sha; Lawrence K. Saul (ICASSP (1), 2006)

Large Margin Hidden Markov Models for Automatic Speech Recognition. - Fei Sha; Lawrence K. Saul (NIPS, 2006)

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

Unsupervised Learning of Image Manifolds by Semidefinite Programming. - Kilian Q. Weinberger; Lawrence K. Saul (Int. J. Comput. Vis., 2006)

IDES: An Internet Distance Estimation Service for Large Networks. - Yun Mao; Lawrence K. Saul; Jonathan M. Smith (IEEE J. Sel. Areas Commun., 2006)

Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models. - Fei Sha; Lawrence K. Saul (ICASSP (4), 2007)

Multiplicative Updates for L1-Regularized Linear and Logistic Regression. - Fei Sha; Y. Albert Park; Lawrence K. Saul (IDA, 2007)

Multiplicative Updates for Nonnegative Quadratic Programming. - Fei Sha; Yuanqing Lin; Lawrence K. Saul; Daniel D. Lee (Neural Comput., 2007)

Nonnegative matrix factorization for real time musical analysis and sight-reading evaluation. - Chih-Chieh Cheng; D. Jingtong Hu; Lawrence K. Saul (ICASSP, 2008)

Fast solvers and efficient implementations for distance metric learning. - Kilian Q. Weinberger; Lawrence K. Saul (ICML, 2008)

Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data. - Joshua M. Lewis; Pincelli M. Hull; Kilian Q. Weinberger; Lawrence K. Saul (ICMLA, 2008)

Large-margin feature adaptation for automatic speech recognition. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (ASRU, 2009)

Sparse decomposition of mixed audio signals by basis pursuit with autoregressive models. - Youngmin Cho; Lawrence K. Saul (ICASSP, 2009)

Matrix updates for perceptron training of continuous density hidden Markov models. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (ICML, 2009)

Learning dictionaries of stable autoregressive models for audio scene analysis. - Youngmin Cho; 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 (ICML, 2009)

A fast online algorithm for large margin training of continuous density hidden Markov models. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (INTERSPEECH, 2009)

A Probabilistic Topic Model for Unsupervised Learning of Musical Key-Profiles. - Diane Hu; Lawrence K. Saul (ISMIR, 2009)

Beyond blacklists: learning to detect malicious web sites from suspicious URLs. - Justin Ma; Lawrence K. Saul; Stefan Savage; Geoffrey M. Voelker (KDD, 2009)

Kernel Methods for Deep Learning. - Youngmin Cho; Lawrence K. Saul (NIPS, 2009)

Technical perspective - The ultimate pilot program. - Stuart Russell 001; Lawrence K. Saul (Commun. ACM, 2009)

Distance Metric Learning for Large Margin Nearest Neighbor Classification. - Kilian Q. Weinberger; Lawrence K. Saul (J. Mach. Learn. Res., 2009)

URL Reputation. - Justin Ma; Lawrence K. Saul; Stefan Savage; Geoffrey M. Voelker (UCI Machine Learning Repository, 2009)

Beyond heuristics: learning to classify vulnerabilities and predict exploits. - Mehran Bozorgi; Lawrence K. Saul; Stefan Savage; Geoffrey M. Voelker (KDD, 2010)

Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development. - Diane Hu; Laurens van der Maaten; Youngmin Cho; Lawrence K. Saul; Sorin Lerner (NIPS, 2010)

Exploiting Feature Covariance in High-Dimensional Online Learning. - Justin Ma; Alex Kulesza; Mark Dredze; Koby Crammer; Lawrence K. Saul; Fernando Pereira 003 (AISTATS, 2010)

Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models. - Chih-Chieh Cheng; Fei Sha; Lawrence K. Saul (IEEE J. Sel. Top. Signal Process., 2010)

Large-Margin Classification in Infinite Neural Networks. - Youngmin Cho; Lawrence K. Saul (Neural Comput., 2010)

Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]. - Kilian Q. Weinberger; Fei Sha; Lawrence K. Saul (IEEE Signal Process. Mag., 2010)

Judging a site by its content: learning the textual, structural, and visual features of malicious web pages. - Sushma Nagesh Bannur; Lawrence K. Saul; Stefan Savage (AISec, 2011)

Topic modeling of freelance job postings to monitor web service abuse. - Do-kyum Kim; Marti Motoyama; Geoffrey M. Voelker; Lawrence K. Saul (AISec, 2011)

Online learning of large margin hidden Markov models for automatic speech recognition. - Lawrence K. Saul; Chih-Chieh Cheng; Fei Sha (MLSLP, 2011)

Maximum Covariance Unfolding : Manifold Learning for Bimodal Data. - Vijay Mahadevan; Chi Wah Wong; José Costa Pereira; Tom Liu; Nuno Vasconcelos; Lawrence K. Saul (NIPS, 2011)

Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification - Youngmin Cho; Lawrence K. Saul (CoRR, 2011)

Nonnegative Matrix Factorization for Semi-supervised Dimensionality Reduction - Youngmin Cho; Lawrence K. Saul (CoRR, 2011)

Hidden-Unit Conditional Random Fields. - Laurens van der Maaten; Max Welling; Lawrence K. Saul (AISTATS, 2011)

Learning to detect malicious URLs. - Justin Ma; Lawrence K. Saul; Stefan Savage; Geoffrey M. Voelker (ACM Trans. Intell. Syst. Technol., 2011)

Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning. - Matthew F. Der; Lawrence K. Saul (NIPS, 2012)

A Variational Approximation for Topic Modeling of Hierarchical Corpora. - Do-kyum Kim; Geoffrey M. Voelker; Lawrence K. Saul (ICML (2), 2013)

eDoctor: Automatically Diagnosing Abnormal Battery Drain Issues on Smartphones. - Xiao Ma 014; Peng Huang 005; Xinxin Jin; Pei Wang; Soyeon Park; Dongcai Shen; Yuanyuan Zhou 001; Lawrence K. Saul; Geoffrey M. Voelker (NSDI, 2013)

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

Search + Seizure: The Effectiveness of Interventions on SEO Campaigns. - David Y. Wang; Matthew F. Der; Mohammad Karami; Lawrence K. Saul; Damon McCoy; Stefan Savage; Geoffrey M. Voelker (Internet Measurement Conference, 2014)

Knock it off: profiling the online storefronts of counterfeit merchandise. - Matthew F. Der; Lawrence K. Saul; Stefan Savage; Geoffrey M. Voelker (KDD, 2014)

Topic Modeling of Hierarchical Corpora. - Do-kyum Kim; Geoffrey M. Voelker; Lawrence K. Saul (CoRR, 2014)

From .academy to .zone: An Analysis of the New TLD Land Rush. - Tristan Halvorson; Matthew F. Der; Ian D. Foster; Stefan Savage; Lawrence K. Saul; Geoffrey M. Voelker (Internet Measurement Conference, 2015)

Who is .com?: Learning to Parse WHOIS Records. - Suqi Liu; Ian D. Foster; Stefan Savage; Geoffrey M. Voelker; Lawrence K. Saul (Internet Measurement Conference, 2015)

Failure analysis and prediction for the CIPRES science gateway. - Kritika Singh; Shava Smallen; Sameer Tilak; Lawrence K. Saul (Concurr. Comput. Pract. Exp., 2016)

On the (In)effectiveness of Mosaicing and Blurring as Tools for Document Redaction. - Steven Hill; Zhimin Zhou; Lawrence K. Saul; Hovav Shacham (Proc. Priv. Enhancing Technol., 2016)

HIV Risk on Twitter: the Ethical Dimension of Social Media Evidence-based Prevention for Vulnerable Populations. - Nadir Weibel; Purvi Desai; Lawrence K. Saul; Amarnath Gupta; Susan Little (HICSS, 2017)

SpeakQL: Towards Speech-driven Multi-modal Querying. - Dharmil Chandarana; Vraj Shah; Arun Kumar 001; Lawrence K. Saul (HILDA@SIGMOD, 2017)

Using Machine Learning to Predict Path-Based Slack from Graph-Based Timing Analysis. - Andrew B. Kahng; Uday Mallappa; Lawrence K. Saul (ICCD, 2018)

"Unobserved Corner" Prediction: Reducing Timing Analysis Effort for Faster Design Convergence in Advanced-Node Design. - Andrew B. Kahng; Uday Mallappa; Lawrence K. Saul; Shangyuan Tong (DATE, 2019)

Measuring Security Practices and How They Impact Security. - Louis F. DeKoven; Audrey Randall; Ariana Mirian; Gautam Akiwate; Ansel Blume; Lawrence K. Saul; Aaron Schulman; Geoffrey M. Voelker; Stefan Savage (Internet Measurement Conference, 2019)

Demonstration of SpeakQL: Speech-driven Multimodal Querying of Structured Data. - Vraj Shah; Side Li; Kevin Yang; Arun Kumar 001; Lawrence K. Saul (SIGMOD Conference, 2019)

Generating correctness proofs with neural networks. - Alex Sanchez-Stern; Yousef Alhessi; Lawrence K. Saul; Sorin Lerner (MAPL@PLDI, 2020)

SpeakQL: Towards Speech-driven Multimodal Querying of Structured Data. - Vraj Shah; Side Li; Arun Kumar 001; Lawrence K. Saul (SIGMOD Conference, 2020)

An online passive-aggressive algorithm for difference-of-squares classification. - Lawrence K. Saul (NeurIPS, 2021)

An EM Algorithm for Capsule Regression. - Lawrence K. Saul (Neural Comput., 2021)

Measuring security practices. - Louis F. DeKoven; Audrey Randall; Ariana Mirian; Gautam Akiwate; Ansel Blume; Lawrence K. Saul; Aaron Schulman; Geoffrey M. Voelker; Stefan Savage (Commun. ACM, 2022)

A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data. - Lawrence K. Saul (SIAM J. Math. Data Sci., 2022)

A geometrical connection between sparse and low-rank matrices and its application to manifold learning. - Lawrence K. Saul (Trans. Mach. Learn. Res., 2022)

Variational Inference with Gaussian Score Matching. - Chirag Modi 002; Robert M. Gower; Charles Margossian; Yuling Yao; David M. Blei; Lawrence K. Saul (NeurIPS, 2023)

The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference. - Charles C. Margossian; Lawrence K. Saul (UAI, 2023)

Weight-balancing fixes and flows for deep learning. - Lawrence K. Saul (Trans. Mach. Learn. Res., 2023)

Batch and match: black-box variational inference with a score-based divergence. - Diana Cai; Chirag Modi 002; Loucas Pillaud-Vivien; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (ICML, 2024)

EigenVI: score-based variational inference with orthogonal function expansions. - Diana Cai; Chirag Modi 002; Charles Margossian; Robert M. Gower; David M. Blei; Lawrence K. Saul (NeurIPS, 2024)

An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations. - Charles C. Margossian; Loucas Pillaud-Vivien; Lawrence K. Saul (CoRR, 2024)

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

Batch, match, and patch: low-rank approximations for score-based variational inference. - Chirag Modi 002; Diana Cai; Lawrence K. Saul (AISTATS, 2025)

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