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06/06/2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Selecting hyperparameters in deep learning greatly impacts its effective...
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06/05/2023
Causal Discovery using Bayesian Model Selection
With only observational data on two variables, and without other assumpt...
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04/11/2023
Actually Sparse Variational Gaussian Processes
Gaussian processes (GPs) are typically criticised for their unfavourable...
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11/11/2022
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design. Unfortunat...
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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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06/28/2022
Memory Safe Computations with XLA Compiler
Software packages like TensorFlow and PyTorch are designed to support li...
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04/14/2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Equivariances provide useful inductive biases in neural network modeling...
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02/25/2022
Learning Invariant Weights in Neural Networks
Assumptions about invariances or symmetries in data can significantly in...
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02/22/2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Data augmentation is commonly applied to improve performance of deep lea...
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01/31/2022
SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive ...
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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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07/20/2021
A Bayesian Approach to Invariant Deep Neural Networks
We propose a novel Bayesian neural network architecture that can learn i...
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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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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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05/10/2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
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04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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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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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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02/12/2021
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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01/11/2021
Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks
Infinite width limits of deep neural networks often have tractable forms...
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11/20/2020
Design of Experiments for Verifying Biomolecular Networks
There is a growing trend in molecular and synthetic biology of using mec...
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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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10/27/2020
A Bayesian Perspective on Training Speed and Model Selection
We take a Bayesian perspective to illustrate a connection between traini...
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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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06/10/2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution f...
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06/08/2020
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a pro...
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05/01/2020
On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and ...
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04/07/2020
Capsule Networks – A Probabilistic Perspective
'Capsule' models try to explicitly represent the poses of objects, enfor...
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03/02/2020
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale probl...
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06/22/2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
We implement gradient-based variational inference routines for Wishart a...
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06/13/2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
We identify a new variational inference scheme for dynamical systems who...
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03/08/2019
Rates of Convergence for Sparse Variational Gaussian Process Regression
Excellent variational approximations to Gaussian process posteriors have...
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02/15/2019
Translation Insensitivity for Deep Convolutional Gaussian Processes
Deep learning has been at the foundation of large improvements in image ...
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12/14/2018
Non-Factorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discret...
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12/10/2018
Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation ...
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12/10/2018
Closed-form Inference and Prediction in Gaussian Process State-Space Models
We examine an analytic variational inference scheme for the Gaussian Pro...
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08/16/2018
Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extra...
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09/06/2017
Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into G...
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10/27/2016
GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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06/15/2016
Understanding Probabilistic Sparse Gaussian Process Approximations
Good sparse approximations are essential for practical inference in Gaus...
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02/06/2014
Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle Tutorial
In this tutorial we explain the inference procedures developed for the s...
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02/06/2014