Laurence Aitchison

research

∙ 09/18/2023

Convolutional Deep Kernel Machines

Deep kernel machines (DKMs) are a recently introduced kernel method with...

0 Edward Milsom, et al. ∙

research

∙ 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...

0 Sebastian Ober, et al. ∙

research

∙ 05/18/2023

Massively Parallel Reweighted Wake-Sleep

Reweighted wake-sleep (RWS) is a machine learning method for performing ...

0 Thomas Heap, et al. ∙

research

∙ 02/27/2023

Taylor TD-learning

Many reinforcement learning approaches rely on temporal-difference (TD) ...

0 Michele Garibbo, et al. ∙

research

∙ 02/22/2023

MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning

In Bayesian optimisation, we often seek to minimise the black-box object...

0 Adam X. Yang, et al. ∙

research

∙ 02/08/2023

Decision trees compensate for model misspecification

The best-performing models in ML are not interpretable. If we can explai...

0 Hugh Panton, et al. ∙

research

∙ 02/02/2023

Imitating careful experts to avoid catastrophic events

RL is increasingly being used to control robotic systems that interact c...

0 Jack R. P. Hanslope, et al. ∙

research

∙ 11/29/2022

Machine learning emulation of a local-scale UK climate model

Climate change is causing the intensification of rainfall extremes. Prec...

0 Henry Addison, et al. ∙

research

∙ 09/15/2022

Random initialisations performing above chance and how to find them

Neural networks trained with stochastic gradient descent (SGD) starting ...

0 Frederik Benzing, et al. ∙

research

∙ 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...

0 Michele Garibbo, et al. ∙

research

∙ 06/24/2022

Out of distribution robustness with pre-trained Bayesian neural networks

We develop ShiftMatch, a new training-data-dependent likelihood for out ...

0 Xi Wang, et al. ∙

research

∙ 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...

8 Anupam K. Gupta, et al. ∙

research

∙ 08/30/2021

A fast point solver for deep nonlinear function approximators

Deep kernel processes (DKPs) generalise Bayesian neural networks, but do...

0 Laurence Aitchison, et al. ∙

research

∙ 07/21/2021

A variational approximate posterior for the deep Wishart process

Recent work introduced deep kernel processes as an entirely kernel-based...

0 Sebastian W. Ober, et al. ∙

research

∙ 07/06/2021

InfoNCE is a variational autoencoder

We show that a popular self-supervised learning method, InfoNCE, is a sp...

0 Laurence Aitchison, et al. ∙

research

∙ 06/10/2021

Data augmentation in Bayesian neural networks and the cold posterior effect

Data augmentation is a highly effective approach for improving performan...

0 Seth Nabarro, et al. ∙

research

∙ 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...

0 Vincent Fortuin, et al. ∙

research

∙ 02/27/2021

Variational Laplace for Bayesian neural networks

We develop variational Laplace for Bayesian neural networks (BNNs) which...

0 Ali Unlu, et al. ∙

research

∙ 02/24/2021

A statistical theory of out-of-distribution detection

We introduce a principled approach to detecting out-of-distribution (OOD...

0 Xi Wang, et al. ∙

research

∙ 02/12/2021

Bayesian Neural Network Priors Revisited

Isotropic Gaussian priors are the de facto standard for modern Bayesian ...

16 Vincent Fortuin, et al. ∙

research

∙ 11/20/2020

Gradient Regularisation as Approximate Variational Inference

Variational inference in Bayesian neural networks is usually performed u...

0 Ali Unlu, et al. ∙

research

∙ 10/04/2020

Deep kernel processes

We define deep kernel processes in which positive definite Gram matrices...

0 Laurence Aitchison, et al. ∙

research

∙ 09/24/2020

Legally grounded fairness objectives

Recent work has identified a number of formally incompatible operational...

0 Dylan Holden-Sim, et al. ∙

research

∙ 08/13/2020

A statistical theory of semi-supervised learning

We currently lack a solid statistical understanding of semi-supervised l...

0 Laurence Aitchison, et al. ∙

research

∙ 08/13/2020

A statistical theory of cold posteriors in deep neural networks

To get Bayesian neural networks to perform comparably to standard neural...

0 Laurence Aitchison, et al. ∙

research

∙ 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 ...

0 Sebastian W. Ober, et al. ∙

research

∙ 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 ...

0 Laurence Aitchison, et al. ∙

research

∙ 08/16/2018

Deep Convolutional Networks as shallow Gaussian Processes

We show that the output of a (residual) convolutional neural network (CN...

0 Adrià Garriga-Alonso, et al. ∙

research

∙ 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...

0 Laurence Aitchison, et al. ∙

research

∙ 06/22/2018

Tensor Monte Carlo: particle methods for the GPU era

Multi-sample objectives improve over single-sample estimates by giving t...

0 Laurence Aitchison, et al. ∙

research

∙ 05/28/2018

Discrete flow posteriors for variational inference in discrete dynamical systems

Each training step for a variational autoencoder (VAE) requires us to sa...

0 Laurence Aitchison, et al. ∙

Laurence Aitchison

Featured Co-authors