L
Lawrence K. Saul
Researcher at University of California, San Diego
Publications - 138
Citations - 40154
Lawrence K. Saul is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Hidden Markov model & Nonlinear dimensionality reduction. The author has an hindex of 49, co-authored 133 publications receiving 37255 citations. Previous affiliations of Lawrence K. Saul include Massachusetts Institute of Technology & University of Pennsylvania.
Papers
More filters
Posted Content
Large Deviation Methods for Approximate Probabilistic Inference
Michael Kearns,Lawrence K. Saul +1 more
TL;DR: In this article, the authors study two-layer belief networks of binary random variables in which the conditional probabilities depend monotonically on weighted sums of the parents, and derive rigorous bounds on many probabilities of interest using methods from large deviation theory.
Proceedings Article
Large deviation methods for approximate probabilistic inference
Michael Kearns,Lawrence K. Saul +1 more
TL;DR: Using methods from large deviation theory, rigorous bounds on marginal probabilities such as Pr[children] are derived and rates of convergence for the accuracy of the authors' bounds as a function of network size are proved.
Proceedings Article
Multiplicative Updates for Classification by Mixture Models
Lawrence K. Saul,Daniel D. Lee +1 more
TL;DR: A learning algorithm that retains the main virtues of the Expectation-Maximization algorithm—its guarantee of monotonic improvement, and its absence of tuning parameters—with the added advantage of optimizing a discriminative objective function for mixture models.
Journal ArticleDOI
Learning in boltzmann trees
TL;DR: A large family of Boltzmann machines that can be trained by standard gradient descent, which can have one or more layers of hidden units, with tree-like connectivity, are introduced.
Proceedings ArticleDOI
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 +6 more
TL;DR: Using eight months of empirical crawled data, this paper identifies 52 distinct SEO campaigns, document how well they are able to place search results for sixteen luxury brands, how this capability impacts the dynamics of their order volumes and how well existing interventions undermine this business when employed.