5 papers · 1 filter
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…
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…
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…
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.…