58 citations · 152 across the 23 of their papers we have counts for
13 papers · 1 filter
Locally Equivalent Weights for Multilevel Regression and Poststratification
Ryan Giordano, Alice Cima, Jared Murray +2
Multilevel regression and poststratification (MrP) has become a workhorse method for estimating population quantities from non-probability surveys, and is the primary model-based a…
Sensitivity of MCMC-based analyses to small-data removal
Tin D. Nguyen, Ryan Giordano, Rachael Meager +1
If the conclusion of a data analysis is sensitive to dropping very few data points, that conclusion might hinge on the particular data at hand rather than representing a more broad…
The Bayesian Infinitesimal Jackknife for Variance
Ryan Giordano, Tamara Broderick
The frequentist variability of Bayesian posterior expectations can provide meaningful measures of uncertainty even when models are misspecified. Classical methods to asymptotically…
Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (Rejoinder)
Ryan Giordano, Runjing Liu, Michael I. Jordan +1
One can typically form a local robustness metric for a particular problem quite directly, for Markov chain Monte Carlo applications as well as optimization problems such as variati…
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Renato Berlinghieri, Brian L. Trippe, David R. Burt +5
Given sparse observations of buoy velocities, oceanographers are interested in reconstructing ocean currents away from the buoys and identifying divergences in a current vector fie…
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…
An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?
Tamara Broderick, Ryan Giordano, Rachael Meager
Study samples often differ from the target populations of inference and policy decisions in non-random ways. Researchers typically believe that such departures from random sampling…
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 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…
Covariances, Robustness, and Variational Bayes
Ryan Giordano, Tamara Broderick, Michael I. Jordan
Mean-field Variational Bayes (MFVB) is an approximate Bayesian posterior inference technique that is increasingly popular due to its fast runtimes on large-scale datasets. However,…
Fast Measurements of Robustness to Changing Priors in Variational Bayes
Ryan Giordano, Tamara Broderick, Michael Jordan
In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of…
Robust Inference with Variational Bayes
Ryan Giordano, Tamara Broderick, Michael Jordan
In Bayesian analysis, the posterior follows from the data and a choice of a prior and a likelihood. One hopes that the posterior is robust to reasonable variation in the choice of…