4 citations · 8 across the 5 of their papers we have counts for
7 papers
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics
Ryan Giordano, Runjing Liu, Michael I. Jordan +1
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 learnin…
A Higher-Order Swiss Army Infinitesimal Jackknife
Ryan Giordano, Michael I. Jordan, Tamara Broderick
Cross validation (CV) and the bootstrap are ubiquitous model-agnostic tools for assessing the error or variability of machine learning and statistical estimators. However, these me…
A Swiss Army Infinitesimal Jackknife
Ryan Giordano, Will Stephenson, Runjing Liu +2
The error or variability of machine learning algorithms is often assessed by repeatedly re-fitting a model with different weighted versions of the observed data. The ubiquitous too…
Cataloging the Visible Universe through Bayesian Inference at Petascale
Jeffrey Regier, Kiran Pamnany, Keno Fischer +9
Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data…
Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap
Ryan Giordano, Runjing Liu, Nelle Varoquaux +2
Clustering procedures typically estimate which data points are clustered together, a quantity of primary importance in many analyses. Often used as a preliminary step for dimension…
Fast robustness quantification with variational Bayes
Ryan Giordano, Tamara Broderick, Rachael Meager +2
Bayesian hierarchical models are increasing popular in economics. When using hierarchical models, it is useful not only to calculate posterior expectations, but also to measure the…