1 citations · 1 across the 2 of their papers we have counts for
4 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…
Rao-Blackwellized Stochastic Gradients for Discrete Distributions
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni +2
We wish to compute the gradient of an expectation over a finite or countably infinite sample space having categories. When is indeed infinite, or finite but ver…
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…
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…