andrewphoto

Andrew Gordon Wilson

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, Computer Science, Statistics, and CAM. In the Fall 2017 semester I am teaching a course on Bayesian Machine Learning.  In the Spring 2018 semester I am teaching a course on Information Theory, Probabilistic Modelling, and Deep Learning.  I am organizing a NIPS 2017 symposium on Interpretable Machine Learning

I am interested in developing flexible, interpretable, and scalable machine learning models, particularly for kernel learning, deep learning, and Gaussian processes.  I am particularly excited about probabilistic approaches.  My work has been applied to time series, vision, NLP, spatial statistics, public policy, medicine, and physics.
 
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
News

Four papers accepted to NIPS 2017, including a spotlight and an oral! Code, pre-prints, and updates to come soon!

NIPS 2017 Symposium on Interpretable Machine Learning!

I am an Area Chair for AAAI 2018!

I am giving a talk at CMStatistics 2017!

Code and tutorials using kernel methods for large scale representation learning

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

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

My thesis provides an introduction to probabilistic non-parametric model construction, Gaussian processes and kernel design, and a vision for scalable and automatic kernel learning, with ideas for future directions.

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

Bayesian GAN
Yunus Saatchi and Andrew Gordon Wilson
Appearing in NIPS 2017
Spotlight
[PDF, arXiv, BibTeX, Code - Coming Soon!]

Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I Frazier
Appearing in NIPS 2017
Oral Presentation
[PDF, arXiv, BibTeX]

Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
Appearing in NIPS 2017
Paper and code coming soon!

Scalable Levy Process Priors for Spectral Kernel Learning
Andrew Loeb, Phillip Jang, Matthew Davidow, and Andrew Gordon Wilson
Appearing in NIPS 2017
Paper and code coming soon!

Multimodal Word Distributions
Ben Athiwaratkun and Andrew Gordon Wilson
Association for Computational Linguistics (ACL), 2017
[arXiv, PDF, 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), 2017.
[PDF, arXiv, Code, 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]

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]