26 citations · 36 across the 3 of their papers we have counts for
4 papers
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…
Neural Manifold Ordinary Differential Equations
Aaron Lou, Derek Lim, Isay Katsman +4
To better conform to data geometry, recent deep generative modelling techniques adapt Euclidean constructions to non-Euclidean spaces. In this paper, we study normalizing flows on…