Jeffrey Regier

According to our database, Jeffrey Regier authored at least 24 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Variational Inference with Coverage Guarantees in Simulation-Based Inference.

Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference.

Declan McNamara, Jackson Loper, Jeffrey Regier

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Variational Inference for Deblending Crowded Starfields.

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration

J. Mach. Learn. Res., 2023

Diffusion Models for Probabilistic Deconvolution of Galaxy Images.

Zhiwei Xue, Yuhang Li, Yash P. Patel, Jeffrey Regier

CoRR, 2023

Variational Inference with Coverage Guarantees.

Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

CoRR, 2023

2022

Dynamic Survival Transformers for Causal Inference with Electronic Health Records.

Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier

CoRR, 2022

Normalizing Flows for Knockoff-free Controlled Feature Selection.

Derek Hansen, Brian Manzo, Jeffrey Regier

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Variational Inference for Deblending Crowded Starfields.

Runjing Liu, Jon D. McAuliffe, Jeffrey Regier

CoRR, 2021

2020

Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data.

Tianci Liu, Jeffrey Regier

CoRR, 2020

Decision-Making with Auto-Encoding Variational Bayes.

Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Cataloging the visible universe through Bayesian inference in Julia at petascale.

Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat

J. Parallel Distributed Comput., 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

Rao-Blackwellized Stochastic Gradients for Discrete Distributions.

Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Approximate Inference for Constructing Astronomical Catalogs from Images.

Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

CoRR, 2018

Stochastic Cubic Regularization for Fast Nonconvex Optimization.

Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Information Constraints on Auto-Encoding Variational Bayes.

Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Cataloging the Visible Universe Through Bayesian Inference at Petascale.

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium, 2018

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

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization.

Jeffrey Regier, Michael I. Jordan, Jon McAuliffe

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

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

2015

Mini-Minimax Uncertainty Quantification for Emulators.

Jeffrey C. Regier, Philip B. Stark

SIAM/ASA J. Uncertain. Quantification, 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

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

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

Proceedings of the 32nd International Conference on Machine Learning, 2015