58 citations · 152 across the 23 of their papers we have counts for
23 papers
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…
Could dropping a few cells change the takeaways from differential expression?
Miriam Shiffman, Ryan Giordano, Tamara Broderick
Differential expression (DE) plays a fundamental role toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While an…
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…
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Ryan Giordano, Martin Ingram, Tamara Broderick
Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stoc…
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…
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikołaj J. Kasprzak, Ryan Giordano, Tamara Broderick
The Laplace approximation is a popular method for constructing a Gaussian approximation to the Bayesian posterior and thereby approximating the posterior mean and variance. But app…
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…
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…
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…
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…
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…
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Jeffrey Regier, Kiran Pamnany, Ryan Giordano +4
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of as…
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…
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…
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Ryan Giordano, Tamara Broderick, Michael Jordan
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of…
Covariance Matrices and Influence Scores for Mean Field Variational Bayes
Ryan Giordano, Tamara Broderick
Mean field variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of…
Covariance Matrices for Mean Field Variational Bayes
Ryan Giordano, Tamara Broderick
Mean Field Variational Bayes (MFVB) is a popular posterior approximation method due to its fast runtime on large-scale data sets. However, it is well known that a major failing of…