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04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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06/26/2020
Stochastic Differential Equations with Variational Wishart Diffusions
We present a Bayesian non-parametric way of inferring stochastic differe...
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05/14/2019
Deep Gaussian Processes with Importance-Weighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as w...
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10/30/2018
Gaussian Process Conditional Density Estimation
Conditional Density Estimation (CDE) models deal with estimating conditi...
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09/24/2018
Orthogonally Decoupled Variational Gaussian Processes
Gaussian processes (GPs) provide a powerful non-parametric framework for...
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03/24/2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
The natural gradient method has been used effectively in conjugate Gauss...
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05/24/2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Gaussian processes (GPs) are a good choice for function approximation as...
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11/08/2016