Celeste: Variational inference for a generative model of astronomical images. - Jeffrey Regier; Andrew C. Miller; Jon McAuliffe; Ryan P. Adams; Matthew D. Hoffman; Dustin Lang; David Schlegel; Prabhat (ICML, 2015)

A Gaussian Process Model of Quasar Spectral Energy Distributions. - Andrew C. Miller; Albert Wu; Jeffrey Regier; Jon McAuliffe; Dustin Lang; Prabhat; David Schlegel; Ryan P. Adams (NIPS, 2015)

Mini-Minimax Uncertainty Quantification for Emulators. - Jeffrey C. Regier; Philip B. Stark (SIAM/ASA J. Uncertain. Quantification, 2015)

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. - Jeffrey Regier; Kiran Pamnany; Ryan Giordano; Rollin C. Thomas; David Schlegel; Jon McAuliffe; Prabhat (CoRR, 2016)

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. - Jeffrey Regier; Michael I. Jordan; Jon McAuliffe (NIPS, 2017)

A deep generative model for gene expression profiles from single-cell RNA sequencing. - Romain Lopez; Jeffrey Regier; Michael I. Jordan; Nir Yosef (CoRR, 2017)

Cataloging the Visible Universe Through Bayesian Inference at Petascale. - Jeffrey Regier; Kiran Pamnany; Keno Fischer; Andreas Noack 001; Maximilian Lam; Jarrett Revels; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (IPDPS, 2018)

Information Constraints on Auto-Encoding Variational Bayes. - Romain Lopez; Jeffrey Regier; Michael I. Jordan; Nir Yosef (NeurIPS, 2018)

Stochastic Cubic Regularization for Fast Nonconvex Optimization. - Nilesh Tripuraneni; Mitchell Stern; Chi Jin 001; Jeffrey Regier; Michael I. Jordan (NeurIPS, 2018)

Approximate Inference for Constructing Astronomical Catalogs from Images. - Jeffrey Regier; Andrew C. Miller; David Schlegel; Ryan P. Adams; Jon D. McAuliffe; Prabhat (CoRR, 2018)

A Deep Generative Model for Semi-Supervised Classification with Noisy Labels. - Maxime Langevin; Edouard Mehlman; Jeffrey Regier; Romain Lopez; Michael I. Jordan; Nir Yosef (CoRR, 2018)

Rao-Blackwellized Stochastic Gradients for Discrete Distributions. - Runjing Liu; Jeffrey Regier; Nilesh Tripuraneni; Michael I. Jordan; Jon D. McAuliffe (ICML, 2019)

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. - Romain Lopez; Achille Nazaret; Maxime Langevin; Jules Samaran; Jeffrey Regier; Michael I. Jordan; Nir Yosef (CoRR, 2019)

Cataloging the visible universe through Bayesian inference in Julia at petascale. - Jeffrey Regier; Keno Fischer; Kiran Pamnany; Andreas Noack 001; Jarrett Revels; Maximilian Lam; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (J. Parallel Distributed Comput., 2019)

Decision-Making with Auto-Encoding Variational Bayes. - Romain Lopez; Pierre Boyeau; Nir Yosef; Michael I. Jordan; Jeffrey Regier (NeurIPS, 2020)

Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data. - Tianci Liu 003; Jeffrey Regier (CoRR, 2020)

Variational Inference for Deblending Crowded Starfields. - Runjing Liu; Jon D. McAuliffe; Jeffrey Regier (CoRR, 2021)

Normalizing Flows for Knockoff-free Controlled Feature Selection. - Derek Hansen; Brian Manzo; Jeffrey Regier (NeurIPS, 2022)

Dynamic Survival Transformers for Causal Inference with Electronic Health Records. - Prayag Chatha; Yixin Wang; Zhenke Wu; Jeffrey Regier (CoRR, 2022)

Variational Inference with Coverage Guarantees. - Yash P. Patel; Declan McNamara; Jackson Loper; Jeffrey Regier; Ambuj Tewari (CoRR, 2023)

Diffusion Models for Probabilistic Deconvolution of Galaxy Images. - Zhiwei Xue; Yuhang Li; Yash P. Patel; Jeffrey Regier (CoRR, 2023)

Variational Inference for Deblending Crowded Starfields. - Runjing Liu; Jon D. McAuliffe; Jeffrey Regier; LSST Dark Energy Science Collaboration (J. Mach. Learn. Res., 2023)

Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference. - Declan McNamara; Jackson Loper; Jeffrey Regier (AISTATS, 2024)

Variational Inference with Coverage Guarantees in Simulation-Based Inference. - Yash P. Patel; Declan McNamara; Jackson Loper; Jeffrey Regier; Ambuj Tewari (ICML, 2024)

Globally Convergent Variational Inference. - Declan McNamara; Jackson Loper; Jeffrey Regier (NeurIPS, 2024)

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions. - Aakash Patel; Tianqing Zhang; Camille Avestruz; Jeffrey Regier; LSST Dark Energy Science Collaboration (CoRR, 2025)