528 citations · 4.4k across the 100 of their papers we have counts for
6 papers · 1 filter
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Wilson, Hannes Nickisch +4
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance st…
Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov +1
We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we t…
Thoughts on Massively Scalable Gaussian Processes
Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch
We introduce a framework and early results for massively scalable Gaussian processes (MSGP), significantly extending the KISS-GP approach of Wilson and Nickisch (2015). The MSGP fr…
The Human Kernel
Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas +1
Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and a…
Bayesian Nonparametric Kernel-Learning
Junier Oliva, Avinava Dubey, Andrew G. Wilson +3
Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approxima…
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)
Andrew Gordon Wilson, Hannes Nickisch
We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce…