125 citations · 1.2k across the 71 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…
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Trevor Campbell, Tamara Broderick
Coherent uncertainty quantification is a key strength of Bayesian methods. But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior unc…