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02/16/2023
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimizat...
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01/24/2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesia...
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06/06/2022
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesia...
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07/21/2021
A variational approximate posterior for the deep Wishart process
Recent work introduced deep kernel processes as an entirely kernel-based...
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06/14/2021
Last Layer Marginal Likelihood for Invariance Learning
Data augmentation is often used to incorporate inductive biases into mod...
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02/24/2021
The Promises and Pitfalls of Deep Kernel Learning
Deep kernel learning and related techniques promise to combine the repre...
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11/18/2020
Understanding Variational Inference in Function-Space
Recent work has attempted to directly approximate the `function-space' o...
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10/04/2020
Deep kernel processes
We define deep kernel processes in which positive definite Gram matrices...
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05/17/2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Variational inference is a popular approach to reason about uncertainty ...
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12/18/2019