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ORCIDJeffrey Regier
Person information
- affiliation: University of Michigan - Ann Arbor, Department of Statistics, Ann Arbor, MI, USA
- affiliation (former): University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, Berkeley, CA, USA
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2020 – today
- 2025
[i22]Declan McNamara, Jackson Loper, Jeffrey Regier:
Globally Convergent Variational Inference. CoRR abs/2501.08201 (2025)- [i21]Aakash Patel, Tianqing Zhang, Camille Avestruz, Jeffrey Regier, LSST Dark Energy Science Collaboration:
Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions. CoRR abs/2503.00156 (2025) - [i20]Jeffrey Regier:
Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images. CoRR abs/2510.03074 (2025) - [i19]Yicun Duan, Xinyue Li, Camille Avestruz, Jeffrey Regier:
Neural Posterior Estimation for Cataloging Astronomical Images from the Legacy Survey of Space and Time. CoRR abs/2510.15315 (2025) - 2024
- [c12]Declan McNamara, Jackson Loper, Jeffrey Regier:
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference. AISTATS 2024: 4312-4320 - [c11]Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari:
Variational Inference with Coverage Guarantees in Simulation-Based Inference. ICML 2024 - [c10]Declan McNamara, Jackson Loper, Jeffrey Regier:
Globally Convergent Variational Inference. NeurIPS 2024 - [i18]Declan McNamara, Jackson Loper, Jeffrey Regier:
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference. CoRR abs/2403.10610 (2024) - 2023
- [j3]Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, LSST Dark Energy Science Collaboration:
Variational Inference for Deblending Crowded Starfields. J. Mach. Learn. Res. 24: 179:1-179:36 (2023) - [i17]Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari:
Variational Inference with Coverage Guarantees. CoRR abs/2305.14275 (2023) - [i16]Zhiwei Xue, Yuhang Li, Yash P. Patel, Jeffrey Regier:
Diffusion Models for Probabilistic Deconvolution of Galaxy Images. CoRR abs/2307.11122 (2023) - 2022
- [c9]Derek Hansen, Brian Manzo, Jeffrey Regier:
Normalizing Flows for Knockoff-free Controlled Feature Selection. NeurIPS 2022 - [i15]Prayag Chatha, Yixin Wang, Zhenke Wu, Jeffrey Regier:
Dynamic Survival Transformers for Causal Inference with Electronic Health Records. CoRR abs/2210.15417 (2022) - 2021
- [i14]Runjing Liu, Jon D. McAuliffe, Jeffrey Regier:
Variational Inference for Deblending Crowded Starfields. CoRR abs/2102.02409 (2021) - [i13]Derek Hansen, Brian Manzo, Jeffrey Regier:
Normalizing Flows for Knockoff-free Controlled Feature Selection. CoRR abs/2106.01528 (2021) - 2020
- [c8]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. NeurIPS 2020 - [i12]Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, Jeffrey Regier:
Decision-Making with Auto-Encoding Variational Bayes. CoRR abs/2002.07217 (2020) - [i11]Tianci Liu, Jeffrey Regier:
Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data. CoRR abs/2006.10175 (2020)
2010 – 2019
- 2019
- [j2]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: 89-104 (2019) - [c7]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019: 4023-4031 - [i10]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 abs/1905.02269 (2019) - 2018
- [c6]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: 44-53 - [c5]Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS 2018: 2904-2913 - [c4]Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
Information Constraints on Auto-Encoding Variational Bayes. NeurIPS 2018: 6117-6128 - [i9]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 abs/1801.10277 (2018) - [i8]Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat:
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR abs/1803.00113 (2018) - [i7]Romain Lopez, Jeffrey Regier, Nir Yosef, Michael I. Jordan:
Information Constraints on Auto-Encoding Variational Bayes. CoRR abs/1805.08672 (2018) - [i6]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 abs/1809.05957 (2018) - [i5]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR abs/1810.04777 (2018) - 2017
- [c3]Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. NIPS 2017: 2402-2411 - [i4]Jeffrey Regier, Michael I. Jordan, Jon McAuliffe:
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization. CoRR abs/1706.02375 (2017) - [i3]Romain Lopez, Jeffrey Regier, Michael I. Jordan, Nir Yosef:
A deep generative model for gene expression profiles from single-cell RNA sequencing. CoRR abs/1709.02082 (2017) - [i2]Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR abs/1711.02838 (2017) - 2016
- [i1]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 abs/1611.03404 (2016) - 2015
- [j1]Jeffrey C. Regier, Philip B. Stark:
Mini-Minimax Uncertainty Quantification for Emulators. SIAM/ASA J. Uncertain. Quantification 3(1): 686-708 (2015) - [c2]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: 2095-2103 - [c1]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: 2494-2502
Coauthor Index
aka: Jon McAuliffe
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