Search Results for author: Jeffrey Regier

Found 22 papers, 14 papers with code

Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference

1 code implementation15 Mar 2024 Declan McNamara, Jackson Loper, Jeffrey Regier

As an alternative, we propose SMC-Wake, a procedure for fitting an amortized variational approximation that uses likelihood-tempered sequential Monte Carlo samplers to estimate the gradient of the inclusive KL divergence.

Variational Inference

Diffusion Models for Probabilistic Deconvolution of Galaxy Images

1 code implementation20 Jul 2023 Zhiwei Xue, Yuhang Li, Yash Patel, Jeffrey Regier

As an alternative, we propose a classifier-free conditional diffusion model for PSF deconvolution of galaxy images.

Diversity

Amortized Variational Inference with Coverage Guarantees

no code implementations23 May 2023 Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation.

Variational Inference

Scalable Bayesian Inference for Detection and Deblending in Astronomical Images

1 code implementation12 Jul 2022 Derek Hansen, Ismael Mendoza, Runjing Liu, Ziteng Pang, Zhe Zhao, Camille Avestruz, Jeffrey Regier

We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS).

Bayesian Inference Variational Inference

Normalizing Flows for Knockoff-free Controlled Feature Selection

1 code implementation3 Jun 2021 Derek Hansen, Brian Manzo, Jeffrey Regier

We propose a new procedure called FlowSelect to perform controlled feature selection that does not suffer from either of these two problems.

Density Estimation feature selection +1

An Empirical Comparison of GANs and Normalizing Flows for Density Estimation

1 code implementation17 Jun 2020 Tianci Liu, Jeffrey Regier

Generative adversarial networks (GANs) and normalizing flows are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prior distribution to an approximation of the data distribution.

Density Estimation

Decision-Making with Auto-Encoding Variational Bayes

2 code implementations NeurIPS 2020 Romain Lopez, Pierre Boyeau, Nir Yosef, Michael. I. Jordan, Jeffrey Regier

To make decisions based on a model fit with auto-encoding variational Bayes (AEVB), practitioners often let the variational distribution serve as a surrogate for the posterior distribution.

Decision Making Two-sample testing

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

2 code implementations6 May 2019 Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael. I. Jordan, Nir Yosef

Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes.

Domain Adaptation Imputation

Rao-Blackwellized Stochastic Gradients for Discrete Distributions

1 code implementation10 Oct 2018 Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael. I. Jordan, Jon McAuliffe

We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories.

General Classification

Information Constraints on Auto-Encoding Variational Bayes

no code implementations NeurIPS 2018 Romain Lopez, Jeffrey Regier, Michael. I. Jordan, Nir Yosef

We show how to apply this method to a range of problems, including the problems of learning invariant representations and the learning of interpretable representations.

Approximate Inference for Constructing Astronomical Catalogs from Images

1 code implementation28 Feb 2018 Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets.

Variational Inference

Cataloging the Visible Universe through Bayesian Inference at Petascale

1 code implementation31 Jan 2018 Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin Thomas, Prabhat

We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia.

Distributed, Parallel, and Cluster Computing Instrumentation and Methods for Astrophysics 85A35, 68W10, 62P35 J.2; D.1.3; G.3; I.2; D.2

Stochastic Cubic Regularization for Fast Nonconvex Optimization

no code implementations NeurIPS 2018 Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael. I. Jordan

This paper proposes a stochastic variant of a classic algorithm---the cubic-regularized Newton method [Nesterov and Polyak 2006].

A deep generative model for gene expression profiles from single-cell RNA sequencing

2 code implementations7 Sep 2017 Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef

We also extend our framework to account for batch effects and other confounding factors, and propose a Bayesian hypothesis test for differential expression that outperforms DESeq2.

Stochastic Optimization Variational Inference

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

no code implementations NeurIPS 2017 Jeffrey Regier, Michael. I. Jordan, Jon McAuliffe

We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick.

Variational Inference

Celeste: Variational inference for a generative model of astronomical images

no code implementations3 Jun 2015 Jeffrey Regier, Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat

We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.

Variational Inference

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