136 citations · 203 across the 6 of their papers we have counts for
7 papers
Variational Inference with Gaussian Score Matching
Chirag Modi, Charles Margossian, Yuling Yao +3
Variational inference (VI) is a method to approximate the computationally intractable posterior distributions that arise in Bayesian statistics. Typically, VI fits a simple paramet…
The Shrinkage-Delinkage Trade-off: An Analysis of Factorized Gaussian Approximations for Variational Inference
Charles C. Margossian, Lawrence K. Saul
When factorized approximations are used for variational inference (VI), they tend to underestimate the uncertainty -- as measured in various ways -- of the distributions they are m…
Generating Correctness Proofs with Neural Networks
Alex Sanchez-Stern, Yousef Alhessi, Lawrence Saul +1
Foundational verification allows programmers to build software which has been empirically shown to have high levels of assurance in a variety of important domains. However, the cos…
Large Deviation Methods for Approximate Probabilistic Inference
Michael Kearns, Lawrence Saul
We study two-layer belief networks of binary random variables in which the conditional probabilities Pr[childlparents] depend monotonically on weighted sums of the parents. In larg…
Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification
Youngmin Cho, Lawrence K. Saul
We investigate a recently proposed family of positive-definite kernels that mimic the computation in large neural networks. We examine the properties of these kernels using tools f…
Aggregate and mixed-order Markov models for statistical language processing
Lawrence Saul, Fernando Pereira
We consider the use of language models whose size and accuracy are intermediate between different order n-gram models. Two types of models are studied in particular. Aggregate Mark…