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11/04/2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
The kernel function and its hyperparameters are the central model select...
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10/14/2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
As Gaussian processes mature, they are increasingly being deployed as pa...
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05/15/2022
A Note on the Chernoff Bound for Random Variables in the Unit Interval
The Chernoff bound is a well-known tool for obtaining a high probability...
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02/23/2022
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Bayesian neural networks (BNNs) combine the expressive power of deep lea...
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09/20/2021
Barely Biased Learning for Gaussian Process Regression
Recent work in scalable approximate Gaussian process regression has disc...
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06/07/2021
How Tight Can PAC-Bayes be in the Small Data Regime?
In this paper, we investigate the question: Given a small number of data...
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02/16/2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
We propose a lower bound on the log marginal likelihood of Gaussian proc...
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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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08/01/2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile a...
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06/23/2020
Variational Orthogonal Features
Sparse stochastic variational inference allows Gaussian process models t...
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01/28/2020
Bandit optimisation of functions in the Matérn kernel RKHS
We consider the problem of optimising functions in the Reproducing kerne...
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09/02/2019
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks
Neural networks provide state-of-the-art performance on a variety of tas...
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03/08/2019
