Bayesian GAN

1 code implementation • NeurIPS 2017 • Yunus Saatci, Andrew G. Wilson

Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood.

Scalable Levy Process Priors for Spectral Kernel Learning

no code implementations • NeurIPS 2017 • Phillip A. Jang, Andrew Loeb, Matthew Davidow, Andrew G. Wilson

We propose a distribution over kernels formed by modelling a spectral mixture density with a Levy process.

Gaussian Processes

Bayesian Nonparametric Kernel-Learning

no code implementations • 29 Jun 2015 • Junier Oliva, Avinava Dubey, Andrew G. Wilson, Barnabas Poczos, Jeff Schneider, Eric P. Xing

In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels.

Fast Kernel Learning for Multidimensional Pattern Extrapolation

no code implementations • NeurIPS 2014 • Andrew G. Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham

This difficulty is compounded by the fact that Gaussian processes are typically only tractable for small datasets, and scaling an expressive kernel learning approach poses different challenges than scaling a standard Gaussian process model.

Gaussian Processes

Copula Processes

no code implementations • NeurIPS 2010 • Andrew G. Wilson, Zoubin Ghahramani

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions.

Bayesian Inference