1 citations · 1 across the 4 of their papers we have counts for
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Simulation-Based Empirical Bayes
Xinwei Shen, Diana Cai, Cheng Zhang +1
Empirical Bayes (EB) performs simultaneous inference across many related latent variables. Classical EB assumes that the likelihood p(x | z) is tractable. In many scientific applic…
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…
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…
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…
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Diana Cai, Ryan P. Adams
Markov chain Monte Carlo (MCMC) is an established approach for uncertainty quantification and propagation in scientific applications. A key challenge in applying MCMC to scientific…