andrewphoto

Andrew Gordon Wilson

News: I have recently moved to Cornell University as an assistant professor. 
I look forward to meeting with admitted Cornell PhD students interested in machine learning research.  I advise students in ORIE and Computer Science. In the Fall 2016 semester I taught a course on Bayesian Machine Learning.  In the Spring 2017 semester I am teaching a course on Information Theory, Probabilistic Modelling, and Deep Learning.  I recently organized a NIPS workshop on Interpretable Machine Learning.

I am interested in developing flexible, interpretable, and scalable machine learning models, particularly for kernel learning and deep learning.  I have expertise in probabilistic modelling, Gaussian processes, Bayesian nonparametrics, kernel methods, neural networks, scalable algorithms, and automatic machine learning.  My work has been applied to time series, image, and video extrapolation, geostatistics, gene expression, natural sound modelling, kernel discovery, Bayesian optimisation, econometrics, cognitive science, NMR spectroscopy, PET imaging, and general relativity. 
 
Outside of work, I am a classical pianist who particularly enjoys Glenn Gould's playing of Bach.

I can be reached at andrew@cornell.edu, and on Twitter @andrewgwils.

Andrew Gordon Wilson
Assistant Professor
235 Rhodes Hall
Cornell University
Recent Highlights

NIPS 2016 Workshop on Interpretable Machine Learning

Code and tutorials using kernel methods for large scale representation learning

A video overviewing my research interests:
Scalable Gaussian Processes for Scientific Discovery
EPFL, Lausanne, Swizterland, February 2016

Stochastic Variational Deep Learning
Andrew Gordon Wilson*, Zhiting Hu*, Ruslan Salakhutdinov, and Eric P. Xing
Neural Information Processing Systems (NIPS), 2016
[PDF, arXiv, Video, Code, BibTeX]

Learning Scalable Deep Kernels with Recurrent Structure
Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing
To appear in the Journal of Machine Learning Research (JMLR).
arXiv pre-print, 2016
[PDF, arXiv, Code, BibTeX]

Deep Kernel Learning
Andrew Gordon Wilson*, Zhiting Hu*, Ruslan Salakhutdinov, and Eric P. Xing
Artificial Intelligence and Statistics (AISTATS), 2016
[PDF, arXiv, BibTeX]

Thoughts on Massively Scalable Gaussian Processes
Andrew Gordon Wilson, Christoph Dann, and Hannes Nickisch
arXiv pre-print, 2015
(See KISS-GP and Deep Kernel Learning for more empirical demonstrations).
[arXiv, PDF, BibTeX, Music]

The Human Kernel
Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, and Eric P. Xing
Neural Information Processing Systems (NIPS), 2015
Spotlight
[PDF, arXiv, Supplement, BibTeX]

Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
Andrew Gordon Wilson and Hannes Nickisch
International Conference on Machine Learning (ICML), 2015
Oral Presentation
[PDF, Supplement, arXiv, BibTeX, Theme Song, Video Lecture]

A video to watch for a succinct introduction to some of my research interests:
Video lecture on KISS-GP (Scalable Gaussian Processes), Lille, France, July 2015

Thesis

In January 2014 I completed my PhD dissertation, "Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian processes" (news story), in the Machine Learning Group at the University of Cambridge, where I am a member of Trinity College

Kernel methods, such as Gaussian processes, have great potential for developing intelligent systems, since the kernel flexibly and interpretably controls the generalisation properties of these methods.  The predictive performance of a kernel method is in general extremely sensitive to the choice of kernel.  However, it is standard practice to use a simple RBF (aka Gaussian, or squared exponential) kernel, which is limited to smoothing and interpolation.  This thesis argues for the importance of developing new kernels, introduces new kernels for automatic pattern extrapolation (with a view towards feature extraction, representation learning, and automatic kernel selection), and discusses how to best scale flexible kernel learning approaches, in order to extract rich structure from large  multidimensional datasets.

Covariance kernels for fast automatic pattern discovery and extrapolation with Gaussian processes
Andrew Gordon Wilson
PhD Thesis, January 2014.
[PDF, BibTeX]
Papers

Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I Frazier
arXiv pre-print, 2016, Coming Soon!
[PDF, arXiv, BibTeX]

Stochastic Variational Deep Learning
Andrew Gordon Wilson*, Zhiting Hu*, Ruslan Salakhutdinov, and Eric P. Xing
Neural Information Processing Systems (NIPS), 2016
[PDF, arXiv, Video, Code, BibTeX]

Learning Scalable Deep Kernels with Recurrent Structure
Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing
To appear in the Journal of Machine Learning Research (JMLR).
arXiv pre-print, 2016
[PDF, arXiv, Code, BibTeX]

Deep Kernel Learning
Andrew Gordon Wilson*, Zhiting Hu*, Ruslan Salakhutdinov, and Eric P. Xing
Artificial Intelligence and Statistics (AISTATS), 2016
[PDF, arXiv, BibTeX]

Thoughts on Massively Scalable Gaussian Processes
Andrew Gordon Wilson, Christoph Dann, and Hannes Nickisch
arXiv pre-print, 2015
(See KISS-GP and Deep Kernel Learning for more empirical demonstrations).
[arXiv, PDF, BibTeX, Music]

Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces

William Herlands, Andrew Gordon Wilson, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, and Eric P. Xing
Artificial Intelligence and Statistics (AISTATS), 2016
[PDF, BibTeX]

Bayesian nonparametric kernel learning
Junier Oliva*, Avinava Dubey*, Andrew Gordon Wilson, Barnabas Poczos, Jeff Schneider, and Eric P. Xing. 
Artificial Intelligence and Statistics (AISTATS), 2016
[PDF, BibTeX]

The human kernel
Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, and Eric P. Xing
Neural Information Processing Systems (NIPS), 2015
Spotlight
[PDF, arXiv, Supplement, BibTeX]

Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
Andrew Gordon Wilson and Hannes Nickisch
International Conference on Machine Learning (ICML), 2015
Oral Presentation
[PDF, Supplement, arXiv, BibTeX, Theme Song, Video Lecture]

Fast kronecker inference in Gaussian processes with non-Gaussian likelihoods
Seth Flaxman, Andrew Gordon Wilson, Daniel Neill, Hannes Nickisch, and Alexander J. Smola
International Conference on Machine Learning (ICML), 2015
Oral Presentation
[PDF, Supplement, BibTeX, Code, Video Lecture]

À la carte - learning fast kernels
Zichao Yang, Alexander J. Smola, Le Song, and Andrew Gordon Wilson
Artificial Intelligence and Statistics (AISTATS), 2015
Oral Presentation
[PDF, BibTeX]

Fast kernel learning for multidimensional pattern extrapolation
Andrew Gordon Wilson*, Elad Gilboa*, Arye Nehorai, and John P. Cunningham
Advances in Neural Information Processing Systems (NIPS) 2014
[PDF, BibTeX, Code, Slides]

Variational inference for latent variable modelling of correlation structure
Mark van der Wilk, Andrew Gordon Wilson, Carl Edward Rasmussen
NIPS Workshop on Advances in Variational Inference, 2014
[PDF, BibTeX]

A Bayesian method to quantifying chemical composition using NMR: application to porous media systems
Yuting Wu, Daniel J. Holland, Mick D. Mantle, Andrew Gordon Wilson, Sebastian Nowozin, Andrew Blake, and Lynn F. Gladden
European Signal Processing Conference (EUSIPCO), 2014
[PDF]

Bayesian inference for NMR spectroscopy with applications to chemical quantification
Andrew Gordon Wilson, Yuting Wu, Daniel J. Holland, Sebastian Nowozin, Mick D. Mantle, Lynn F. Gladden, and Andrew Blake
In Submission
. February 14, 2014
[arXiv, PDF, BibTeX]

Covariance kernels for fast automatic pattern discovery and extrapolation with Gaussian processes
Andrew Gordon Wilson
PhD Thesis, January 2014
[PDF, BibTeX]

Student-t
processes as alternatives to Gaussian processes
Amar Shah, Andrew Gordon Wilson, and Zoubin Ghahramani
Artificial Intelligence and Statistics, 2014
[arXiv, PDF, Supplementary, BibTeX]

The change point kernel
Andrew Gordon Wilson
Technical Report (Note), University of Cambridge.
November 2013.
[PDF, BibTeX]

GPatt: Fast multidimensional pattern extrapolation with Gaussian processes
Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, and John P. Cunningham
October 21, 2013.   In Submission.
[arXiv, PDF, BibTeX, Resources and Tutorial]

Bayesian optimization using Student-t processes
Amar Shah, Andrew Gordon Wilson, and Zoubin Ghahramani
NIPS Workshop on Bayesian Optimisation, 2013.
[PDF, BibTeX]

Gaussian process kernels for pattern discovery and extrapolation
Andrew Gordon Wilson and Ryan Prescott Adams
International Conference on Machine Learning (ICML), 2013.
Oral Presentation
[arXiv, PDF, Correction, Supplementary, BibTeX, Slides, Resources and Tutorial, Video Lecture]

Modelling input varying correlations between multiple responses
Andrew Gordon Wilson and Zoubin Ghahramani
European Conference on Machine Learning (ECML), 2012
Nectar Track  for "significant machine learning results"
Oral Presentation

[PDF, BibTeX]

A process over all stationary covariance kernels
Andrew Gordon Wilson
Technical Report, University of Cambridge.
June 2012.
[PDF, BibTeX]

Gaussian process regression networks
Andrew Gordon Wilson, David A. Knowles, and Zoubin Ghahramani
International Conference on Machine Learning (ICML), 2012.
Oral Presentation
[PDF, BibTeX, Slides, Supplementary, Video Lecture, Original Code, New Code]

Generalised Wishart processes
Andrew Gordon Wilson and Zoubin Ghahramani
Uncertainty in Artificial Intelligence (UAI), 2011.
Best Student Paper Award
[PDF, BibTeX]

Copula processes
Andrew Gordon Wilson and Zoubin Ghahramani
Advances in Neural Information Processing Systems (NIPS), 2010.
Spotlight

[PDF, BibTeX, Slides, Video Lecture]