125 citations · 1.2k across the 71 of their papers we have counts for
5 papers · 1 filter
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…
Independent finite approximations for Bayesian nonparametric inference
Tin D. Nguyen, Jonathan Huggins, Lorenzo Masoero +2
Completely random measures (CRMs) and their normalizations (NCRMs) offer flexible models in Bayesian nonparametrics. But their infinite dimensionality presents challenges for infer…
Approximate Cross-Validation with Low-Rank Data in High Dimensions
William T. Stephenson, Madeleine Udell, Tamara Broderick
Many recent advances in machine learning are driven by a challenging trifecta: large data size ; high dimensions; and expensive algorithms. In this setting, cross-validation (CV…
Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (…
Approximate Cross-Validation for Structured Models
Soumya Ghosh, William T. Stephenson, Tin D. Nguyen +2
Many modern data analyses benefit from explicitly modeling dependence structure in data -- such as measurements across time or space, ordered words in a sentence, or genes in a gen…