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…
Geometric Causal Models
Eli N. Weinstein, David M. Blei
Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. W…
Bayesian Invariance Modeling of Multi-Environment Data
Luhuan Wu, Mingzhang Yin, Yixin Wang +2
Invariant prediction [Peters et al., 2016] analyzes feature/outcome data from multiple environments to identify invariant features - those with a stable predictive relationship to…
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…
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth +2
Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily…
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.…