World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
52
Citations
46310
World Ranking
4934
National Ranking
2293

Overview

Lawrence K. Saul is affiliated with the University of California, San Diego in the United States. Their research contributions primarily fall within the field of Computer Science, with specific focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing, and Information Systems.

The main topics addressed in their work include Bayesian Methods and Mixture Models, Gaussian Processes and Bayesian Inference, Face and Expression Recognition, Sparse and Compressive Sensing Techniques, Domain Adaptation and Few-Shot Learning, Statistical Methods and Inference, and Generative Adversarial Networks and Image Synthesis.

Saul has authored several papers, notable among them are:

  • A tractable latent variable model for nonlinear dimensionality reduction, 2020, Proceedings of the National Academy of Sciences
  • A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data, 2022, SIAM Journal on Mathematics of Data Science
  • Measuring security practices, 2022, Communications of the ACM
  • Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs, 2024, arXiv (Cornell University)
  • The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference, 2023, arXiv (Cornell University)

Their frequent coauthors include Charles C. Margossian, Chirag Modi, David M. Blei, Diana Cai, and Loucas Pillaud-Vivien.

Saul's publications appear regularly in venues such as arXiv (Cornell University), Proceedings of the National Academy of Sciences, SIAM Journal on Mathematics of Data Science, Communications of the ACM, and Neural Computation.

Best Publications

  • Nonlinear dimensionality reduction by locally linear embedding.

    Sam T. Roweis;Lawrence K. Saul

  • Distance Metric Learning for Large Margin Nearest Neighbor Classification

    Kilian Q. Weinberger;Lawrence K. Saul

  • An introduction to variational methods for graphical models

    Michael I. Jordan;Zoubin Ghahramani;Tommi S. Jaakkola;Lawrence K. Saul

  • Distance Metric Learning for Large Margin Nearest Neighbor Classification

    Kilian Q. Weinberger;John Blitzer;Lawrence K. Saul

  • Unsupervised Learning of Image Manifolds by Semidefinite Programming

    Kilian Q. Weinberger;Lawrence K. Saul

  • Think globally, fit locally: unsupervised learning of low dimensional manifolds

    Lawrence K. Saul;Sam T. Roweis

  • Beyond blacklists: learning to detect malicious web sites from suspicious URLs

    Justin Ma;Lawrence K. Saul;Stefan Savage;Geoffrey M. Voelker

  • Identifying suspicious URLs: an application of large-scale online learning

    Justin Ma;Lawrence K. Saul;Stefan Savage;Geoffrey M. Voelker

  • Learning a kernel matrix for nonlinear dimensionality reduction

    Kilian Q. Weinberger;Fei Sha;Lawrence K. Saul

  • Kernel Methods for Deep Learning

    Youngmin Cho;Lawrence K. Saul

  • Mean field theory for sigmoid belief networks

    Lawrence K. Saul;Tommi Jaakkola;Michael I. Jordan

  • Fast solvers and efficient implementations for distance metric learning

    Kilian Q. Weinberger;Lawrence K. Saul

  • Multiplicative Updates for Nonnegative Quadratic Programming

    Fei Sha;Yuanqing Lin;Lawrence K. Saul;Daniel D. Lee

  • Spectral Methods for Dimensionality Reduction.

    Lawrence K. Saul;Kilian Q. Weinberger;Fei Sha;Jihun Ham

  • Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines

    Fei Sha;Lawrence K. Saul;Daniel D. Lee

  • An introduction to nonlinear dimensionality reduction by maximum variance unfolding

    Killan Q. Weinberger;Lawrence K. Saul

  • Exploiting Tractable Substructures in Intractable Networks

    Lawrence K. Saul;Michael I. Jordan

  • Semisupervised alignment of manifolds.

    Jihun Ham;Daniel D. Lee;Lawrence K. Saul

  • An Introduction to Locally Linear Embedding

    Lawrence K. Saul;Sam T. Roweis

  • Global Coordination of Local Linear Models

    Sam T. Roweis;Lawrence K. Saul;Geoffrey E. Hinton

Frequent Co-Authors

Fei Sha
Fei Sha Facebook (United States)
Kilian Q. Weinberger
Kilian Q. Weinberger Cornell University
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Geoffrey M. Voelker
Geoffrey M. Voelker University of California, San Diego
Stefan Savage
Stefan Savage University of California, San Diego
Jont B. Allen
Jont B. Allen University of Illinois at Urbana-Champaign
Sam T. Roweis
Sam T. Roweis New York University
Fernando Pereira
Fernando Pereira Google (United States)

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