publications

2024

  • Globally Convergent Variational Inference
    Declan McNamara, Jackson Loper, and Jeffrey Regier
    Neural Information Processing Systems (NeurIPS) 2024
    [pdf] [code]
  • Variational Inference with Coverage Guarantees
    Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, and Ambuj Tewari
    International Conference on Machine Learning (ICML) 2024
    [pdf] [code]
  • Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
    Declan McNamara, Jackson Loper, and Jeffrey Regier
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2024
    [pdf] [code]

2023

  • Variational inference for deblending crowded starfields
    Runjing Liu, Jon D. McAuliffe, Jeffrey Regier, and The LSST Dark Energy Science Collaboration
    Journal of Machine Learning Research 2023
    [pdf] [code]
  • Sequential Monte Carlo for Detecting and Deblending Objects in Astronomical Images
    Tim White, and Jeffrey Regier
    NeurIPS Workshop on Machine Learning and the Physical Sciences 2023
    [pdf] [code]
  • Simulation-Based Inference for Detecting Blending in Spectra
    Declan McNamara, and Jeffrey Regier
    NeurIPS Workshop on Machine Learning and the Physical Sciences 2023
    [pdf] [code]
  • Improving accuracy in cell perturbation experiments by leveraging auxiliary information
    Jackson Loper, Noam Solomon, and Jeffrey Regier
    arXiv: 2307.11686 2023
    [pdf]
  • 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]
  • 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]
  • 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]
  • The scverse project provides a computational ecosystem for single-cell omics data analysis
    Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Philipp Angerer, Volker Bergen, Pierre Boyeau, Maren Büttner, Gokcen Eraslan, David Fischer, Max Frank, Justin Hong, Michal Klein, Marius Lange, Romain Lopez, Mohammad Lotfollahi, Malte D. Luecken, Fidel Ramirez, Jeffrey Regier, Sergei Rybakov, Anna C. Schaar, Valeh Valiollah Pour Amiri, Philipp Weiler, Galen Xing, Bonnie Berger, Dana Pe’er, Aviv Regev, Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle, and Fabian J. Theis
    Nature Biotechnology 2023
    [pdf]

2022

  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • Normalizing flows for knockoff-free controlled feature selection
    Derek Hansen, Brian Manzo, and Jeffrey Regier
    Neural Information Processing Systems (NeurIPS) 2022
    [pdf] [code]

2021

  • 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]
  • 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]
  • 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

  • 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]
  • 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

  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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]
  • 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

  • Deep generative modeling for single-cell transcriptomics
    Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, and Nir Yosef
    Nature Methods 2018
    [pdf] [code]
  • 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]
  • 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]
  • 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]
  • 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

  • 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]
  • 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]
  • 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

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

2015

  • Mini-minimax uncertainty quantification for emulators
    Jeffrey Regier, and Philip B. Stark
    SIAM/ASA Journal on Uncertainty Quantification 2015
    [code] [pdf]
  • 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]
  • 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]
  • 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

  • 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]