2 citations · 2 across the 8 of their papers we have counts for
9 papers
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…
Fisher meets Feynman: score-based variational inference with a product of experts
Diana Cai, Robert M. Gower, David M. Blei +1
We introduce a highly expressive yet distinctly tractable family for black-box variational inference (BBVI). Each member of this family is a weighted product of experts (PoE), and…
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
Ningyuan Huang, Richard Stiskalek, Jun-Young Lee +6
Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature…
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.…
Batch, match, and patch: low-rank approximations for score-based variational inference
Chirag Modi, Diana Cai, Lawrence K. Saul
Black-box variational inference (BBVI) scales poorly to high-dimensional problems when it is used to estimate a multivariate Gaussian approximation with a full covariance matrix. I…
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…
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…
Topic Modeling of Hierarchical Corpora
Do-kyum Kim, Geoffrey M. Voelker, Lawrence K. Saul
We study the problem of topic modeling in corpora whose documents are organized in a multi-level hierarchy. We explore a parametric approach to this problem, assuming that the numb…