publications

2023

1. Variational inference for deblending crowded starfields Runjing Liu, Jon D. McAuliffe, and Jeffrey Regier Journal of Machine Learning Research 2023 [pdf] [code]
2. Improving accuracy in cell perturbation experiments by leveraging auxiliary information Jackson Loper, Noam Solomon, and Jeffrey Regier arXiv: 2307.11686 2023 [pdf]
3. Diffusion models for probabilistic deconvolution of galaxy images Zhiwei Xue, Yuhang Li, Yash Patel, and Jeffrey Regier ICML Workshop on Machine Learning for Astrophysics 2023 [pdf] [code]
4. Variational inference with coverage guarantees Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, and Ambuj Tewari arXiv: 2305.14275 2023 [pdf]
5. Model-free error assessment for breadth-first studies, with applications to cell perturbation experiments Jackson Loper, Robert Barton, Meena Subramaniam, Maxime Dhainaut, and Jeffrey Regier arXiv: 2208.01745 2023 [pdf] [code]
6. An empirical Bayes method for differential expression analysis of single cells with deep generative models Pierre Boyeau, Jeffrey Regier, Adam Gayoso, Michael Jordan, Romain Lopez, and Nir Yosef Proceedings of the National Academy of Sciences 2023 [pdf] [code]

2022

1. Scalable Bayesian inference for detection and deblending in astronomical images Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, and Jeffrey Regier ICML Workshop on Machine Learning for Astrophysics 2022 [pdf] [code]
2. Statistical inference for coadded astronomical images Mallory Wang, Ismael Mendoza, Cheng Wang, Camille Avestruz, and Jeffrey Regier NeurIPS Workshop on Machine Learning and the Physical Sciences 2022 [pdf] [code]
3. Dynamic survival transformers for causal inference with electronic health records Prayag Chatha, Yixin Wang, Zhenke Wu, and Jeffrey Regier NeurIPS Workshop on Learning from Time Series for Health 2022 [pdf]
4. Scalable Bayesian inference for detecting strong gravitational lensing systems Yash Patel, and Jeffrey Regier NeurIPS Workshop on Machine Learning and the Physical Sciences 2022 [pdf] [code]
5. A Python library for probabilistic analysis of single-cell omics data Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong, Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, Yining Liu, Jules Samaran, Gabriel Misrachi, Achille Nazaret, Oscar Clivio, Chenling Xu, Tal Ashuach, Mariano Gabitto, Mohammad Lotfollahi, Valentine Svensson, Eduardo Veiga Beltrame, Vitalii Kleshchevnikov, Carlos Talavera-López, Lior Pachter, Fabian J. Theis, Aaron Streets, Michael I. Jordan, Jeffrey Regier, and Nir Yosef Nature Biotechnology 2022 [pdf] [code]
6. Normalizing flows for knockoff-free controlled feature selection Derek Hansen, Brian Manzo, and Jeffrey Regier Neural Information Processing Systems (NeurIPS) 2022 [pdf] [code]

2021

1. An empirical comparison of GANs and normalizing flows for density estimation Tianci Liu, and Jeffrey Regier NeurIPS Workshop on Bayesian Deep Learning 2021 [pdf] [code]
2. Joint probabilistic modeling of single-cell multi-omic data with totalVI Adam Gayoso, Zoë Steier, Romain Lopez, Jeffrey Regier, Kristopher Nazor, Aaron Streets, and Nir Yosef Nature Methods 2021 [pdf] [code]
3. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models Chenling Xu, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I. Jordan, and Nir Yosef Molecular Systems Biology 2021 [pdf] [code]

2020

1. Decision-making with Auto-Encoding Variational Bayes Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, and Jeffrey Regier Neural Information Processing Systems (NeurIPS) 2020 [pdf] [code]
2. Cell-type annotation priors for scRNA-seq Oscar Clivio, Drausin Wulsin, Evgeny Kiner, Noam Solomon, Luis Voloch, and Jeffrey Regier Machine Learning in Computational Biology (MLCB) Meeting 2020 [pdf]

2019

1. Approximate inference for constructing astronomical catalogs from images Jeffrey Regier, Andrew Miller, David Schlegel, Ryan P. Adams, Jon McAuliffe, and Prabhat Annals of Applied Statistics 2019 [pdf] [code]
2. A joint model of RNA expression and surface protein abundance in single cells Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets, and Nir Yosef Machine Learning in Computational Biology (MLCB) Meeting 2019 [pdf] [code]
3. Deep generative models for detecting differential expression in single cells Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, and Nir Yosef Machine Learning in Computational Biology (MLCB) Meeting 2019 [pdf] [code]
4. Detecting zero-inflated genes in single-cell transcriptomics data Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, and Nir Yosef Machine Learning in Computational Biology (MLCB) Meeting 2019 [pdf] [code]
5. 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 Jordan, and Nir Yosef ICML Workshop on Computational Biology 2019 [pdf] [code]
6. Rao-Blackwellized stochastic gradients for discrete distributions Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, and Jon McAuliffe International Conference on Machine Learning (ICML) 2019 [pdf] [code]
7. 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 Thomas, and Prabhat Journal of Parallel and Distributed Computing 2019 [pdf] [code]

2018

1. Deep generative modeling for single-cell transcriptomics Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, and Nir Yosef Nature Methods 2018 [pdf] [code]
2. Stochastic cubic regularization for fast nonconvex optimization Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, and Michael I. Jordan Neural Information Processing Systems (NeurIPS) 2018 [pdf]
3. Information constraints on Auto-Encoding Variational Bayes Romain Lopez, Jeffrey Regier, Michael I. Jordan, and Nir Yosef Neural Information Processing Systems (NeurIPS) 2018 [pdf] [code]
4. 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 Thomas, and Prabhat International Parallel and Distributed Processing Symposium (IPDPS) 2018 [pdf] [code]
5. A deep generative model for semi-supervised classification with noisy labels Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, and Nir Yosef Bay Area Machine Learning Symposium 2018 [pdf] [code]

2017

1. Fast black-box variational inference through stochastic trust-region optimization Jeffrey Regier, Michael I. Jordan, and Jon McAuliffe Neural Information Processing Systems (NIPS) 2017 [pdf]
2. A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, and Nir Yosef NIPS Workshop on Machine Learning in Computational Biology 2017 [pdf] [code]
3. A deep generative model for single-cell RNA sequencing Romain Lopez, Jeffrey Regier, Michael I. Jordan, and Nir Yosef Bay Area Machine Learning Symposium 2017 [pdf] [code]

2016

1. Topics in large-scale statistical inference Jeffrey Regier 2016 [pdf]
2. Second-order stochastic variational inference Jeffrey Regier, and Jon McAuliffe Bay Area Machine Learning Symposium 2016 [pdf]

2015

1. Mini-minimax uncertainty quantification for emulators Jeffrey Regier, and Philip B. Stark SIAM/ASA Journal on Uncertainty Quantification 2015 [code] [pdf]
2. A deep generative model for astronomical images of galaxies Jeffrey Regier, Jon McAuliffe, and Prabhat NIPS Workshop on Advances in Approximate Bayesian Inference 2015 [pdf]
3. A Gaussian process model of quasar spectral energy distributions Andrew Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, and Ryan Adams Neural Information Processing Systems (NIPS) 2015 [pdf]
4. Celeste: Variational inference for a generative model of astronomical images Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, and Prabhat International Conference on Machine Learning (ICML) 2015 [pdf] [code]

2014

1. Celeste: Scalable variational inference for a generative model of astronomical images Jeffrey Regier, Brenton Partridge, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, and Prabhat NIPS Workshop on Advances in Variational Inference 2014 [code] [pdf]