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06/20/2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty ...
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10/10/2022
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, wi...
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06/17/2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has recei...
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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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10/15/2018
Successor Uncertainties: exploration and uncertainty in temporal difference learning
We consider the problem of balancing exploration and exploitation in seq...
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07/01/2018
Learning to Drive in a Day
We demonstrate the first application of deep reinforcement learning to a...
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12/05/2017
Learning a Generative Model for Validity in Complex Discrete Structures
Deep generative models have been successfully used to learn representati...
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08/15/2017
Actively Learning what makes a Discrete Sequence Valid
Deep learning techniques have been hugely successful for traditional sup...
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11/21/2016