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Andrew Gordon Wilson

I aim to develop a prescriptive approach to building autonomous intelligent systems. This effort involves a variety of different research initiatives, which cumulatively work together towards achieving this vision. A major theme that unifies many of these initiatives is a desire for an actionable understanding, so that we can select for particular properties aligned with human goals. These areas, and some example papers, include:
Understanding deep learning models, including LLMs and vision models
[e.g., 1, 2, 3, 4, 29, 30, 31]
Uncertainty representation, Bayesian methods, online decision making
[e.g., 1, 5, 6, 7]
Distribution shifts, spurious correlations [e.g., 8, 9, 10, 11]
Encoding and learning inductive biases (e.g., equivariances) [e.g., 12, 13, 14, 15]
Numerical linear algebra for scalable inference [e.g., 16, 17, 18, 19, 20]
Machine learning for physics, and physics for ML [e.g., 21, 22, 13, 20, 15]
Simple practical methods [e.g., 23, 24, 25, 26, 4]
Scientific discovery (protein engineering, materials design) [e.g., 27, 28, 32]

Outside of work, I am a classical pianist who particularly enjoys Glenn Gould's playing of Bach.

I can be reached at andrewgw@cims.nyu.edu, and on Twitter @andrewgwils.

Andrew Gordon Wilson
Associate Professor
Courant Institute of Mathematical Sciences and Center for Data Science
60 Fifth Ave
New York University
News


I have received an NSF CAREER Award to develop new foundations in Bayesian deep learning! I'm grateful to my students, collaborators, and everyone who has supported our work over the years. Stay tuned for updates on research under this award!

SWA is now natively implemented in PyTorch 1.6! SWA exploits the geometric structure of loss surfaces to significantly improve the generalization of many optimizers, including SGD and Adam, at no additional costs. SWA has also found many applications, including semi-supervised learning, deep RL, uncertainty representation, Bayesian deep learning, calibration, low precision training... I encourage you try it out! It's easy, and also works on pre-trained models.

I presented an ICML 2020 tutorial on Bayesian deep learning!
Papers

Google scholar page



This listing of papers is out of date. Please see my CV for an updated list.

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2022.
[PDF (Coming Soon!), arXiv (Coming Soon!), code (Coming Soon!), BibTeX]

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2022.
[PDF, arXiv, code, BibTeX]

On Feature Learning in the Presence of Spurious Correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2022.
[PDF, arXiv, code, BibTeX]

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Sanyam Kapoor, Wesley Maddox, Pavel Izmailov, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2022.
[PDF, arXiv, code, BibTeX]

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2022.
[PDF (Coming Soon), arXiv (Coming soon), code (Coming Soon), BibTeX]

Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2022.
Outstanding Paper Award
[PDF, arXiv, code, BibTeX]

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver, Phil Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2022
[PDF, arXiv, code, BibTeX]

Low-Precision Stochastic Gradient Langevin Dynamics
Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
International Conference on Machine Learning (ICML), 2022
[PDF, arXiv, code, BibTeX]

Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
Gregory Benton, Wesley Maddox, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2022
[PDF, arXiv, code, BibTeX]

Low Precision Arithmetic for Fast Gaussian Processes
Wesley Maddox, Andres Potapczynski, Andrew Gordon Wilson
Uncertainty in Artificial Intelligence (UAI), 2022
[PDF, arXiv, code, BibTeX]

Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction
Jordan Venderley et. al
Proceedings of the National Academy of Sciences (PNAS), 2022
[PDF, arXiv, code, BibTeX]

Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Nate Gruver, Marc Finzi, Andrew Gordon Wilson
International Conference on Learning Representations (ICLR), 2022
[PDF, arXiv, code, BibTeX]

Dangers of Bayesian Model Averaging under Covariate Shift
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2021
[PDF, arXiv, code, BibTeX]

Bayesian Optimization with High-Dimensional Outputs
Wesley Maddox, Max Balandat, Andrew Gordon Wilson, Eytan Bakshy.
Advances in Neural Information Processing Systems (NeurIPS), 2021
[PDF, arXiv, code, BibTeX]

Does Knowledge Distillation Really Work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander Alemi,
Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2021
[PDF, arXiv, BibTeX]

Residual Pathway Priors for Soft Equivariance Constraints
Marc Finzi, Gregory Benton, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2021
[PDF (Coming Soon), arXiv (Coming Soon), code (Coming Soon), BibTeX]

Conditioning Sparse Variational Gaussian Processes for Online Decision-making
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2021
[PDF (Coming Soon), arXiv (Coming Soon), code (Coming Soon), BibTeX]

What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
International conference on Machine Learning (ICML), 2021
Long oral presentation
[PDF, arXiv, code, BibTeX]

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi, Max Welling, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2021
Long oral presentation
[PDF, arXiv, code, documentation, examples, BibTeX]

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Sanyam Kapoor, Marc Finzi, Alex Wang, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2021
Long oral presentation
[PDF, arXiv, code, BibTeX]

Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
Greg Benton, Wesley Maddox, Sanae Lotfi, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2021
[PDF, arXiv, code, BibTeX]

Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger Grosse
International Conference on Machine Learning (ICML), 2021
[PDF, arXiv, code, BibTeX]

On the model-based stochastic value gradient for continuous reinforcement learning
Brandon Amos, Sam Stanton, Denis Yarats, Andrew Gordon Wilson
Learning for Dynamics and Control (L4DC), 2021
[PDF, code, BibTeX]

Kernel Interpolation for Scalable Online Gaussian Processes
Sam Stanton, Wesley Maddox, Ian Delbridge, Andrew Gordon Wilson
Artificial Intelligence and Statistics (AISTATS), 2021
[PDF, arXiv, code, BibTeX]

Fast Adaptation with Linearized Neural Networks
Wesley Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Wilson,
Andreas Damianou
Artificial Intelligence and Statistics (AISTATS), 2021
[PDF, arXiv, code, BibTeX]

Bayesian Deep Learning and a Probabilistic Perspective of Generalization
Andrew Gordon Wilson, Pavel Izmailov
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF, arXiv, code, BibTeX]

Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF, arXiv, code, BibTeX]

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints
Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF, arXiv, code, BibTeX]

Learning Invariances in Neural Networks
Greg Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF, arXiv, code, BibTeX]

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew Gordon Wilson, Eytan Bakshy
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF, arXiv, code, BibTeX]

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric Xing, Zhiting Hu
Advances in Neural Information Processing Systems (NeurIPS), 2020
[PDF, arXiv, code, BibTeX]

Generalizing Convolutional Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2020
[PDF, arXiv, code, BibTeX]

Semi-Supervised Learning with Normalizing Flows
Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2020
[PDF, arXiv, code, BibTeX]

Randomly Projected Additive Gaussian Processes for Regression
Ian Delbridge, David S. Bindel, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2020
[PDF, arXiv, code, BibTeX]

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
International Conference on Learning Representations (ICLR), 2020
[PDF, arXiv, code, BibTeX]

Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Wesley J. Maddox, Gregory Benton, Andrew Gordon Wilson
arXiv pre-print, 2020
[PDF, arXiv, code, BibTeX]

The Case for Bayesian Deep Learning
Andrew Gordon Wilson
Technical Report, NYU Courant, 2019
[PDF, Web Version, BibTeX]

A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley Maddox, Timur Garipov, Pavel Izmailov, Andrew Gordon Wilson
To appear in Advances in Neural Information Processing Systems (NeurIPS), 2019
[PDF, arXiv, code, BibTeX]

Function-Space Distributions over Kernels
Greg Benton, Jayson Salkey, Wesley Maddox, Julio Albinati, Andrew Gordon Wilson
Advances in Neural Information Processing Systems (NeurIPS), 2019.
[PDF, arXiv, code, BibTeX]

Exact Gaussian Processes on a Million Data Points
Ke Alexander Wang, Geoff Pleiss, Jake Gardner, Stephen Tyree, Kilian Weinberger, Andrew Gordon Wilson
To appear in Advances in Neural Information Processing Systems (NeurIPS), 2019
[PDF, arXiv, code, example notebook, BibTeX]

Subspace Inference for Bayesian Deep Learning
Pavel Izmailov*, Wesley Maddox*, Polina Kirichenko*, Timur Garipov*, Dmitry Vetrov, Andrew Gordon Wilson
Uncertainty in Artificial Intelligence (UAI), 2019
[PDF, arXiv, code, BibTeX]

Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning
Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson
Uncertainty in Artificial Intelligence (UAI), 2019
[PDF, arXiv, BibTeX]

SWALP: Stochastic Weight Averaging in Low Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa
International Conference on Machine Learning (ICML), 2019
[PDF, arXiv, code, BibTeX]

SysML: The New Frontier of Machine Learning Systems
A. Ratner et. al, 2019
[PDF, arXiv, BibTeX]

There Are Many Consistent Explanations of Unlabeled Data:
Why You Should Average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
International Conference on Learning Representations (ICLR), 2019
[PDF, arXiv, code, BibTeX]

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson
To appear in the Journal of Machine Learning Research (JMLR), 2019
[PDF, arXiv, BibTeX]

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jake Gardner, Geoff Pleiss, David Bindel, Kilian Weinberger, Andrew Gordon Wilson
Neural Information Processing Systems (NIPS), 2018
Spotlight
[PDF, arXiv, GPyTorch website, GPyTorch repository, BibTeX]

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Timur Garipov*, Pavel Izmailov*, Dmitrii Podoprikhin*, Dmitry Vetrov, Andrew Gordon Wilson
Neural Information Processing Systems (NIPS), 2018
Spotlight
[PDF, arXiv, code, BibTeX]

Scaling Gaussian Process Regression with Derivatives
David Eriksson, Kun Dong, Eric Lee, David Bindel, Andrew Gordon Wilson
Neural Information Processing Systems (NIPS), 2018
[PDF, arXiv, code, BibTeX]

Probabilistic FastText for Multisense Word Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
Association for Computational Linguistics (ACL), 2018
Oral presentation
[PDF, arXiv, code, BibTeX]

Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov*, Dmitrii Podoprikhin*, Timur Garipov*, Dmitry Vetrov, Andrew Gordon Wilson
Uncertainty in Artificial Intelligence (UAI), 2018.
Oral presentation
[PDF, arXiv, code, BibTeX]

Automated Local Regression Discontinuity Design Discovery
William Herlands, Ed McFowland III, Andrew Gordon Wilson, Daniel B. Neill
Knowledge Discovery and Data Mining (KDD), 2018
[PDF, Video, BibTeX]

Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss, Jacob Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
International Conference on Machine Learning (ICML), 2018
[PDF, arXiv, code, BibTeX]

Hierarchical Density Order Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson
International Conference on Learning Representations (ICLR), 2018.
[PDF, arXiv, code, BibTeX]

Product Kernel Interpolation for Scalable Gaussian Processes
Jacob Gardner, Geoff Pleiss, Ruihan Wu, Kilian Weinberger, Andrew Gordon Wilson
Artificial Intelligence and Statistics (AISTATS), 2018.
[PDF, arXiv, code, BibTeX]

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
William Herlands, Ed McFowland, Andrew Gordon Wilson, Daniel B. Neill
Artificial Intelligence and Statistics (AISTATS), 2018.
[PDF, arXiv, BibTeX]

Bayesian GAN
Yunus Saatchi and Andrew Gordon Wilson
Neural Information Processing Systems (NIPS), 2017
Spotlight
[PDF, arXiv, code, Short Video, BibTeX]

Bayesian Optimization with Gradients
Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I Frazier
Neural Information Processing Systems (NIPS), 2017
Oral Presentation
[PDF, arXiv, Code, NIPS Oral Presentation, BibTeX]

Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
Neural Information Processing Systems (NIPS), 2017
[PDF, arXiv, Code, Video (from David E), BibTeX]

Scalable Levy Process Priors for Spectral Kernel Learning
Andrew Loeb, Phillip Jang, Matthew Davidow, and Andrew Gordon Wilson
Neural Information Processing Systems (NIPS), 2017
[PDF, arXiv, Code, Video (from Phillip J), BibTeX]

Multimodal Word Distributions
Ben Athiwaratkun and Andrew Gordon Wilson
Association for Computational Linguistics (ACL), 2017
[PDF, arXiv, 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 [PyTorch, more recent], code [Keras+Matlab], BibTeX]

Stochastic Variational Deep Kernel Learning
Andrew Gordon Wilson*, Zhiting Hu*, Ruslan Salakhutdinov, and Eric P. Xing
Neural Information Processing Systems (NIPS), 2016
[PDF, arXiv, Video, code (GPyTorch, more recent), code (Caffe+Matlab), BibTeX]

Deep Kernel Learning
Andrew Gordon Wilson*, Zhiting Hu*, Ruslan Salakhutdinov, and Eric P. Xing
Artificial Intelligence and Statistics (AISTATS), 2016
[PDF, arXiv, code (GPyTorch, more recent), code (Keras+Matlab), code (Caffe+Matlab), 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).
[PDF, arXiv, code (GPyTorch, more recent), code (older tutorials), 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, code (GPyTorch, newer), code (tutorials, older), 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, GPyTorch implementation, 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]