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09/18/2023
Convolutional Deep Kernel Machines
Deep kernel machines (DKMs) are a recently introduced kernel method with...
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05/23/2023
An Improved Variational Approximate Posterior for the Deep Wishart Process
Deep kernel processes are a recently introduced class of deep Bayesian m...
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05/18/2023
Massively Parallel Reweighted Wake-Sleep
Reweighted wake-sleep (RWS) is a machine learning method for performing ...
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02/27/2023
Taylor TD-learning
Many reinforcement learning approaches rely on temporal-difference (TD) ...
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02/22/2023
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
In Bayesian optimisation, we often seek to minimise the black-box object...
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02/08/2023
Decision trees compensate for model misspecification
The best-performing models in ML are not interpretable. If we can explai...
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02/02/2023
Imitating careful experts to avoid catastrophic events
RL is increasingly being used to control robotic systems that interact c...
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11/29/2022
Machine learning emulation of a local-scale UK climate model
Climate change is causing the intensification of rainfall extremes. Prec...
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09/15/2022
Random initialisations performing above chance and how to find them
Neural networks trained with stochastic gradient descent (SGD) starting ...
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08/23/2022
What deep reinforcement learning tells us about human motor learning and vice-versa
Machine learning and specifically reinforcement learning (RL) has been e...
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06/24/2022
Out of distribution robustness with pre-trained Bayesian neural networks
We develop ShiftMatch, a new training-data-dependent likelihood for out ...
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09/08/2021
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear
Robotic touch, particularly when using soft optical tactile sensors, suf...
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08/30/2021
A fast point solver for deep nonlinear function approximators
Deep kernel processes (DKPs) generalise Bayesian neural networks, but do...
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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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07/06/2021
InfoNCE is a variational autoencoder
We show that a popular self-supervised learning method, InfoNCE, is a sp...
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06/10/2021
Data augmentation in Bayesian neural networks and the cold posterior effect
Data augmentation is a highly effective approach for improving performan...
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05/14/2021
BNNpriors: A library for Bayesian neural network inference with different prior distributions
Bayesian neural networks have shown great promise in many applications w...
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02/27/2021
Variational Laplace for Bayesian neural networks
We develop variational Laplace for Bayesian neural networks (BNNs) which...
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02/24/2021
A statistical theory of out-of-distribution detection
We introduce a principled approach to detecting out-of-distribution (OOD...
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02/12/2021
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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11/20/2020
Gradient Regularisation as Approximate Variational Inference
Variational inference in Bayesian neural networks is usually performed u...
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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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09/24/2020
Legally grounded fairness objectives
Recent work has identified a number of formally incompatible operational...
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08/13/2020
A statistical theory of semi-supervised learning
We currently lack a solid statistical understanding of semi-supervised l...
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08/13/2020
A statistical theory of cold posteriors in deep neural networks
To get Bayesian neural networks to perform comparably to standard neural...
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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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10/17/2019
Why bigger is not always better: on finite and infinite neural networks
Recent work has shown that the outputs of convolutional neural networks ...
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08/16/2018
Deep Convolutional Networks as shallow Gaussian Processes
We show that the output of a (residual) convolutional neural network (CN...
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07/19/2018
A unified theory of adaptive stochastic gradient descent as Bayesian filtering
There are a diverse array of schemes for adaptive stochastic gradient de...
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06/22/2018
Tensor Monte Carlo: particle methods for the GPU era
Multi-sample objectives improve over single-sample estimates by giving t...
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05/28/2018