Search Results for author:
Found 14 papers, 8 papers with code
Could dropping a few cells change the takeaways from differential expression?
We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets.
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI.
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Given sparse observations of buoy velocities, oceanographers are interested in reconstructing ocean currents away from the buoys and identifying divergences in a current vector field.
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
A Higher-Order Swiss Army Infinitesimal Jackknife
The first-order approximation is known as the "infinitesimal jackknife" in the statistics literature and has been the subject of recent interest in machine learning for approximate CV.
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Bayesian models based on the Dirichlet process and other stick-breaking priors have been proposed as core ingredients for clustering, topic modeling, and other unsupervised learning tasks.
Methodology
A Swiss Army Infinitesimal Jackknife
This linear approximation is sometimes known as the "infinitesimal jackknife" in the statistics literature, where it is mostly used to as a theoretical tool to prove asymptotic results.
Methodology
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
Covariances, Robustness, and Variational Bayes
The estimates for MFVB posterior covariances rely on a result from the classical Bayesian robustness literature relating derivatives of posterior expectations to posterior covariances and include the Laplace approximation as a special case.
Methodology
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.
Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics.
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
We call our method linear response variational Bayes (LRVB).
Covariance Matrices and Influence Scores for Mean Field Variational Bayes
We develop a fast, general methodology for exponential families that augments MFVB to deliver accurate uncertainty estimates for model variables -- both for individual variables and coherently across variables.
Covariance Matrices for Mean Field Variational Bayes
We develop a fast, general methodology for exponential families that augments MFVB to deliver accurate uncertainty estimates for model variables -- both for individual variables and coherently across variables.
