2020

  1. Flows Succeed Where GANs Fail: Lessons from Low-Dimensional Data Tianci Liu, and Jeffrey Regier arXiv:2006.10175 2020 [pdf] [code]
  2. Decision-Making with Auto-Encoding Variational Bayes Romain Lopez, Pierre Boyeau, Nir Yosef, Michael I. Jordan, and Jeffrey Regier arXiv:2002.07217 2020 [pdf] [code]
  3. Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells Adam Gayoso, Zoë Steier, Romain Lopez, Jeffrey Regier, Kristopher L Nazor, Aaron Streets, and Nir Yosef bioRxiv 2020 [pdf] [code]

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. 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 bioRxiv 2019 [pdf] [code]
  3. 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 In Machine Learning in Computational Biology (MLCB) Meeting 2019 [pdf] [code]
  4. Deep generative models for detecting differential expression in single cells Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, and Nir Yosef In Machine Learning in Computational Biology (MLCB) Meeting 2019 [pdf] [code]
  5. Detecting zero-inflated genes in single-cell transcriptomics data Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, and Nir Yosef In Machine Learning in Computational Biology (MLCB) Meeting 2019 [pdf] [code]
  6. 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 In ICML Workshop on Computational Biology 2019 [pdf] [code]
  7. Rao-Blackwellized stochastic gradients for discrete distributions Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, and Jon McAuliffe In International Conference on Machine Learning (ICML) 2019 [pdf] [code]
  8. 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 In 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 In 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 In 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 In 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 In 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 In 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 In Bay Area Machine Learning Symposium 2017 [pdf] [code]
  4. Galaxy shape modeling with probabilistic auto-encoders Jeffrey Regier, and Jon McAuliffe 2017

2016

  1. Topics in large-scale statistical inference Jeffrey Regier 2016 [pdf]
  2. Second-order stochastic variational inference Jeffrey Regier, and Jon McAuliffe In 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 In 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 In 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 In 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 In NIPS Workshop on Advances in Variational Inference 2014 [code] [pdf]