331 citations · 504 across the 25 of their papers we have counts for
10 papers · 1 filter
Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models
Jinlin Lai, Charles C. Margossian, Daniel R. Sheldon
Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models and mixed-effect models. Effici…
Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry
Charles C. Margossian, Isaac E. Rankin, Lawrence K. Saul
Variational inference (VI) approximates a target density by the best match in a family of tractable distributions. The best variational approximation is found by minimizing…
EigenVI: score-based variational inference with orthogonal function expansions
Diana Cai, Chirag Modi, Charles C. Margossian +3
We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI). EigenVI constructs its variational approximations from orthogonal function expansions.…
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
Charles C. Margossian, Lawrence K. Saul
Given an intractable target density , variational inference (VI) attempts to find the best approximation from a tractable family . This is typically done by minimizing th…
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
Charles C. Margossian, Loucas Pillaud-Vivien, Lawrence K. Saul
Given an intractable distribution , the problem of variational inference (VI) is to find the best approximation from some more tractable family . Commonly, one chooses to…
Listening to the Noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana +1
In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the dist…
Batch and match: black-box variational inference with a score-based divergence
Diana Cai, Chirag Modi, Loucas Pillaud-Vivien +4
Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge…
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…
Amortized Variational Inference: When and Why?
Charles C. Margossian, David M. Blei
In a probabilistic latent variable model, factorized (or mean-field) variational inference (F-VI) fits a separate parametric distribution for each latent variable. Amortized variat…
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…