4 code implementations • 18 Oct 2018 • Steven R. Howard, Aaditya Ramdas, Jon McAuliffe, Jasjeet Sekhon

A confidence sequence is a sequence of confidence intervals that is uniformly valid over an unbounded time horizon.

Statistics Theory Probability Methodology Statistics Theory

1 code implementation • 10 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.

1 code implementation • 31 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

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.

no code implementations • 10 Nov 2016 • Jeffrey Regier, Kiran Pamnany, Ryan Giordano, Rollin Thomas, David Schlegel, Jon McAuliffe, Prabhat

Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.

no code implementations • NeurIPS 2015 • Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams

We propose a method for combining two sources of astronomical data, spectroscopy and photometry, that carry information about sources of light (e. g., stars, galaxies, and quasars) at extremely different spectral resolutions.

no code implementations • 3 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.

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