Jeffrey Regier

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 28

Top Topics

Variational Inference

Top Venues

Publications

Declan McNamara, Jackson Loper, Jeffrey Regier
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference. AISTATS (2024)
Declan McNamara, Jackson Loper, Jeffrey Regier
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference. CoRR (2024)
Zhiwei Xue, Yuhang Li, Yash Patel, Jeffrey Regier
Diffusion Models for Probabilistic Deconvolution of Galaxy Images. CoRR (2023)
Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari
Variational Inference with Coverage Guarantees. CoRR (2023)
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration
Variational Inference for Deblending Crowded Starfields. J. Mach. Learn. Res. 24 (2023)
Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier
Dynamic Survival Transformers for Causal Inference with Electronic Health Records. CoRR (2022)
Derek Hansen, Brian Manzo, Jeffrey Regier
Normalizing Flows for Knockoff-free Controlled Feature Selection. NeurIPS (2022)
Runjing Liu, Jon D. McAuliffe, Jeffrey Regier
Variational Inference for Deblending Crowded Starfields. CoRR (2021)
Derek Hansen, Brian Manzo, Jeffrey Regier
Normalizing Flows for Knockoff-free Controlled Feature Selection. CoRR (2021)
Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier
Decision-Making with Auto-Encoding Variational Bayes. NeurIPS (2020)
Tianci Liu, Jeffrey Regier
Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data. CoRR (2020)
Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier
Decision-Making with Auto-Encoding Variational Bayes. CoRR (2020)
Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127 (2019)
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML (2019)
Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, Nir Yosef
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. CoRR (2019)
Romain Lopez, Jeffrey Regier, Nir Yosef, Michael I. Jordan
Information Constraints on Auto-Encoding Variational Bayes. CoRR (2018)
Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR (2018)
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR (2018)
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS (2018)
Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS (2018)
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR (2018)
Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels. CoRR (2018)
Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef
Information Constraints on Auto-Encoding Variational Bayes. NeurIPS (2018)
Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef
A deep generative model for gene expression profiles from single-cell RNA sequencing. CoRR (2017)
Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. CoRR (2017)
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR (2017)
Jeffrey Regier, Michael I. Jordan, Jon McAuliffe
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. NIPS (2017)
Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin C. Thomas, David Schlegel, Jon McAuliffe, Prabhat
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. CoRR (2016)
Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams
A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS (2015)
Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat
Celeste: Variational inference for a generative model of astronomical images. ICML (2015)