Andrew Gordon Wilson


Code repositories for group projects and close collaborations.

Implements state-of-the-art scalable Gaussian processes in PyTorch, including:
(1) SKI/KISS-GP [older but helpful tutorials in Matlab here]
(2) Deep Kernel Learning [older but helpful tutorials in Matlab here]
(3) Stochastic Variational Deep Kernel Learning
(4) Scalable Kernel Learning by Stochastic Lanczos Expansions
(5) Spectral Mixture Kernels [older but helpful tutorials in Matlab here]
(6) SKIP (scaling SKI/KISS-GP to higher dimensions)
(7) LOVE (Constant time predictive distributions)

GP Kernel Learning Tutorials
Tutorials for SKI/KISS-GP, Spectral Mixture Kernels, Kronecker Inference, and Deep Kernel Learning. The accompanying code is in Matlab and is now mostly out of date; the implementations in GPyTorch are typically much more efficient. However, the tutorial material and code is still very useful for anyone wanting to understand the building blocks and practical advice for SKI/KISS-GP, Spectral Mixture Kernels, or Kronecker Inference.

Stochastic Weight Averaging (SWA)
SWA is a simple DNN training method that can be used as a drop-in replacement for SGD with improved generalization, faster convergence, and essentially no overhead. In this repository we provide a PyTorch implementation of SWA.

fast-SWA and Semi-Supervised Learning
Provides a PyTorch implementation of fast-SWA and the record breaking semi-supervised results in Improving Consistency Based Semi-Supervised Learning with Weight Averaging.

Implements probabilistic Gaussian mixture word embeddings in Tensorflow.

Implements the Bayesian GAN in Tensorflow.

Hierarchical Density Order Embeddings
Provides a Torch implementation of our ICLR 2018 paper. In this paper we learn hierarchical representations of concepts using encapsulation of probability densities.

Probabilistic FastText
Provides a C++ implementation of our ACL 2018 paper. In this paper we learn density embeddings that account for sub-word structure and multiple senses.

Gaussian Processes for Machine Learning
The iconic GPML toolbox, the official software accompaniment to the Gaussian processes for machine learning textbook. GPML includes native support for Spectral Mixture Kernels, Kronecker Inference, and SKI/KISS-GPTutorials for this material based on GPML can be found here.