Mark van der Wilk

research

∙ 06/06/2023

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Selecting hyperparameters in deep learning greatly impacts its effective...

8 Alexander Immer, et al. ∙

research

∙ 06/05/2023

Causal Discovery using Bayesian Model Selection

With only observational data on two variables, and without other assumpt...

0 Anish Dhir, et al. ∙

research

∙ 04/11/2023

Actually Sparse Variational Gaussian Processes

Gaussian processes (GPs) are typically criticised for their unfavourable...

0 Harry Jake Cunningham, et al. ∙

research

∙ 11/11/2022

Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization

Bayesian Optimization is a useful tool for experiment design. Unfortunat...

0 Jose Pablo Folch, et al. ∙

research

∙ 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...

0 Alexander Terenin, et al. ∙

research

∙ 06/28/2022

Memory Safe Computations with XLA Compiler

Software packages like TensorFlow and PyTorch are designed to support li...

0 Artem Artemev, et al. ∙

research

∙ 04/14/2022

Relaxing Equivariance Constraints with Non-stationary Continuous Filters

Equivariances provide useful inductive biases in neural network modeling...

6 Tycho F. A. van der Ouderaa, et al. ∙

research

∙ 02/25/2022

Learning Invariant Weights in Neural Networks

Assumptions about invariances or symmetries in data can significantly in...

203 Tycho F. A. van der Ouderaa, et al. ∙

research

∙ 02/22/2022

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Data augmentation is commonly applied to improve performance of deep lea...

58 Alexander Immer, et al. ∙

research

∙ 01/31/2022

SnAKe: Bayesian Optimization with Pathwise Exploration

Bayesian Optimization is a very effective tool for optimizing expensive ...

0 Jose Pablo Folch, et al. ∙

research

∙ 09/20/2021

Barely Biased Learning for Gaussian Process Regression

Recent work in scalable approximate Gaussian process regression has disc...

0 David R. Burt, et al. ∙

research

∙ 07/20/2021

A Bayesian Approach to Invariant Deep Neural Networks

We propose a novel Bayesian neural network architecture that can learn i...

0 Nikolaos Mourdoukoutas, et al. ∙

research

∙ 06/14/2021

Last Layer Marginal Likelihood for Invariance Learning

Data augmentation is often used to incorporate inductive biases into mod...

0 Pola Elisabeth Schwöbel, 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

∙ 05/10/2021

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applicati...

23 Vincent Dutordoir, et al. ∙

research

∙ 04/12/2021

GPflux: A Library for Deep Gaussian Processes

We introduce GPflux, a Python library for Bayesian deep learning with a ...

28 Vincent Dutordoir, et al. ∙

research

∙ 02/24/2021

The Promises and Pitfalls of Deep Kernel Learning

Deep kernel learning and related techniques promise to combine the repre...

0 Sebastian W. Ober, et al. ∙

research

∙ 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...

0 Artem Artemev, 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

∙ 01/11/2021

Correlated Weights in Infinite Limits of Deep Convolutional Neural Networks

Infinite width limits of deep neural networks often have tractable forms...

0 Adrià Garriga-Alonso, et al. ∙

research

∙ 11/20/2020

Design of Experiments for Verifying Biomolecular Networks

There is a growing trend in molecular and synthetic biology of using mec...

0 Ruby Sedgwick, et al. ∙

research

∙ 11/18/2020

Understanding Variational Inference in Function-Space

Recent work has attempted to directly approximate the `function-space' o...

0 David R. Burt, et al. ∙

research

∙ 10/27/2020

A Bayesian Perspective on Training Speed and Model Selection

We take a Bayesian perspective to illustrate a connection between traini...

0 Clare Lyle, et al. ∙

research

∙ 08/01/2020

Convergence of Sparse Variational Inference in Gaussian Processes Regression

Gaussian processes are distributions over functions that are versatile a...

3 David R. Burt, et al. ∙

research

∙ 06/23/2020

Variational Orthogonal Features

Sparse stochastic variational inference allows Gaussian process models t...

9 David R. Burt, et al. ∙

research

∙ 06/10/2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

In image segmentation, there is often more than one plausible solution f...

32 Miguel Monteiro, et al. ∙

research

∙ 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...

22 Binxin Ru, et al. ∙

research

∙ 05/01/2020

On the Benefits of Invariance in Neural Networks

Many real world data analysis problems exhibit invariant structure, and ...

6 Clare Lyle, et al. ∙

research

∙ 04/07/2020

Capsule Networks – A Probabilistic Perspective

'Capsule' models try to explicitly represent the poses of objects, enfor...

16 Lewis Smith, et al. ∙

research

∙ 03/02/2020

A Framework for Interdomain and Multioutput Gaussian Processes

One obstacle to the use of Gaussian processes (GPs) in large-scale probl...

11 Mark van der Wilk, et al. ∙

research

∙ 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...

0 Creighton Heaukulani, et al. ∙

research

∙ 06/13/2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

We identify a new variational inference scheme for dynamical systems who...

3 Alessandro Davide Ialongo, et al. ∙

research

∙ 03/08/2019

Rates of Convergence for Sparse Variational Gaussian Process Regression

Excellent variational approximations to Gaussian process posteriors have...

0 David R. Burt, et al. ∙

research

∙ 02/15/2019

Translation Insensitivity for Deep Convolutional Gaussian Processes

Deep learning has been at the foundation of large improvements in image ...

18 Vincent Dutordoir, et al. ∙

research

∙ 12/14/2018

Non-Factorised Variational Inference in Dynamical Systems

We focus on variational inference in dynamical systems where the discret...

4 Alessandro Davide Ialongo, et al. ∙

research

∙ 12/10/2018

Bayesian Layers: A Module for Neural Network Uncertainty

We describe Bayesian Layers, a module designed for fast experimentation ...

14 Dustin Tran, et al. ∙

research

∙ 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...

0 Alessandro Davide Ialongo, et al. ∙

research

∙ 08/16/2018

Learning Invariances using the Marginal Likelihood

Generalising well in supervised learning tasks relies on correctly extra...

0 Mark van der Wilk, et al. ∙

research

∙ 09/06/2017

Convolutional Gaussian Processes

We present a practical way of introducing convolutional structure into G...

0 Mark van der Wilk, et al. ∙

research

∙ 10/27/2016

GPflow: A Gaussian process library using TensorFlow

GPflow is a Gaussian process library that uses TensorFlow for its core c...

0 Alexander G. de G. Matthews, et al. ∙

research

∙ 06/15/2016

Understanding Probabilistic Sparse Gaussian Process Approximations

Good sparse approximations are essential for practical inference in Gaus...

0 Matthias Bauer, et al. ∙

research

∙ 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...

0 Yarin Gal, et al. ∙

research

∙ 02/06/2014

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Gaussian processes (GPs) are a powerful tool for probabilistic inference...

0 Yarin Gal, et al. ∙

Mark van der Wilk

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