27 citations · 50 across the 7 of their papers we have counts for
6 papers · 1 filter
Scalable Bayesian Transformed Gaussian Processes
Xinran Zhu, Leo Huang, Cameron Ibrahim +2
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a j…
Scaling Gaussian Processes with Derivative Information Using Variational Inference
Misha Padidar, Xinran Zhu, Leo Huang +2
Gaussian processes with derivative information are useful in many settings where derivative information is available, including numerous Bayesian optimization and regression tasks…
Density of States Graph Kernels
Leo Huang, Andrew Graven, David Bindel
A fundamental problem on graph-structured data is that of quantifying similarity between graphs. Graph kernels are an established technique for such tasks; in particular, those bas…
Efficient Rollout Strategies for Bayesian Optimization
Eric Hans Lee, David Eriksson, Bolong Cheng +2
Bayesian optimization (BO) is a class of sample-efficient global optimization methods, where a probabilistic model conditioned on previous observations is used to determine future…
Randomly Projected Additive Gaussian Processes for Regression
Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson
Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle…
Scaling Gaussian Process Regression with Derivatives
David Eriksson, Kun Dong, Eric Hans Lee +2
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a G…