4 papers
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…