113 citations · 294 across the 25 of their papers we have counts for
5 papers · 1 filter
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…
Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins +1
Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods…
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is…
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Gaussian processes (GPs) offer a flexible class of priors for nonparametric Bayesian regression, but popular GP posterior inference methods are typically prohibitively slow or lack…
Random Feature Stein Discrepancies
Jonathan H. Huggins, Lester Mackey
Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness…