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Found 7 papers, 3 papers with code
Time-uniform, nonparametric, nonasymptotic confidence sequences
A confidence sequence is a sequence of confidence intervals that is uniformly valid over an unbounded time horizon.
Statistics Theory Probability Methodology Statistics Theory
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories.
Cataloging the Visible Universe through Bayesian Inference at Petascale
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
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick.
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results.
A Gaussian Process Model of Quasar Spectral Energy Distributions
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.
Celeste: Variational inference for a generative model of astronomical images
We present a new, fully generative model of optical telescope image sets, along with a variational procedure for inference.
