25 citations · 45 across the 9 of their papers we have counts for
9 papers
Do covariates explain why these groups differ? The choice of reference group can reverse conclusions in the Oaxaca-Blinder decomposition
Manuel Quintero, Advik Shreekumar, William T. Stephenson +1
Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two hospitals could be due to differences in…
Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
Manuel Quintero, William T. Stephenson, Advik Shreekumar +1
In science and social science, we often wish to explain why an outcome is different in two populations. For instance, if a jobs program benefits members of one city more than anoth…
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
William T. Stephenson, Zachary Frangella, Madeleine Udell +1
Models like LASSO and ridge regression are extensively used in practice due to their interpretability, ease of use, and strong theoretical guarantees. Cross-validation (CV) is wide…
Measuring the robustness of Gaussian processes to kernel choice
William T. Stephenson, Soumya Ghosh, Tin D. Nguyen +3
Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of atmospheric carbon dioxide levels. Notably, the choice of GP…
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…
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…
Approximate Cross-Validation in High Dimensions with Guarantees
William T. Stephenson, Tamara Broderick
Leave-one-out cross-validation (LOOCV) can be particularly accurate among cross-validation (CV) variants for machine learning assessment tasks -- e.g., assessing methods' error or…
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger +4
Until recently, transcriptomics was limited to bulk RNA sequencing, obscuring the underlying expression patterns of individual cells in favor of a global average. Thanks to technol…
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…