113 citations · 294 across the 25 of their papers we have counts for
3 papers · 2 filters
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…
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…