Chris J. Oates

research

∙ 05/17/2023

Stein Π-Importance Sampling

Stein discrepancies have emerged as a powerful tool for retrospective im...

0 Congye Wang, et al. ∙

research

∙ 03/08/2023

Meta-learning Control Variates: Variance Reduction with Limited Data

Control variates can be a powerful tool to reduce the variance of Monte ...

0 Zhuo Sun, et al. ∙

research

∙ 03/05/2023

The Matérn Model: A Journey through Statistics, Numerical Analysis and Machine Learning

The Matérn model has been a cornerstone of spatial statistics for more t...

0 Emilio Porcu, et al. ∙

research

∙ 11/16/2022

Sobolev Spaces, Kernels and Discrepancies over Hyperspheres

This work provides theoretical foundations for kernel methods in the hyp...

0 Simon Hubbert, et al. ∙

research

∙ 10/28/2022

Minimum Kernel Discrepancy Estimators

For two decades, reproducing kernels and their associated discrepancies ...

0 Chris J. Oates, et al. ∙

research

∙ 08/08/2022

Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG

We analyse the calibration of BayesCG under the Krylov prior, a probabil...

0 Tim W. Reid, et al. ∙

research

∙ 07/06/2022

Gradient-Free Kernel Stein Discrepancy

Stein discrepancies have emerged as a powerful statistical tool, being a...

0 Matthew A Fisher, et al. ∙

research

∙ 06/16/2022

Generalised Bayesian Inference for Discrete Intractable Likelihood

Discrete state spaces represent a major computational challenge to stati...

22 Takuo Matsubara, et al. ∙

research

∙ 03/17/2022

Maximum Likelihood Estimation in Gaussian Process Regression is Ill-Posed

Gaussian process regression underpins countless academic and industrial ...

0 Toni Karvonen, et al. ∙

research

∙ 11/15/2021

A Statistical Approach to Surface Metrology for 3D-Printed Stainless Steel

Surface metrology is the area of engineering concerned with the study of...

0 Chris J. Oates, et al. ∙

research

∙ 10/15/2021

GaussED: A Probabilistic Programming Language for Sequential Experimental Design

Sequential algorithms are popular for experimental design, enabling emul...

0 Matthew A Fisher, et al. ∙

research

∙ 09/13/2021

Minimum Discrepancy Methods in Uncertainty Quantification

The lectures were prepared for the École Thématique sur les Incertitudes...

0 Chris J. Oates, et al. ∙

research

∙ 06/15/2021

Black Box Probabilistic Numerics

Probabilistic numerics casts numerical tasks, such the numerical solutio...

0 Onur Teymur, et al. ∙

research

∙ 05/07/2021

Stein's Method Meets Statistics: A Review of Some Recent Developments

Stein's method is a collection of tools for analysing distributional com...

0 Andreas Anastasiou, et al. ∙

research

∙ 04/22/2021

Bayesian Numerical Methods for Nonlinear Partial Differential Equations

The numerical solution of differential equations can be formulated as an...

0 Junyang Wang, et al. ∙

research

∙ 04/15/2021

Robust Generalised Bayesian Inference for Intractable Likelihoods

Generalised Bayesian inference updates prior beliefs using a loss functi...

9 Takuo Matsubara, et al. ∙

research

∙ 03/30/2021

Post-Processing of MCMC

Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statist...

0 Leah F. South, et al. ∙

research

∙ 12/23/2020

Testing whether a Learning Procedure is Calibrated

A learning procedure takes as input a dataset and performs inference for...

0 Jon Cockayne, et al. ∙

research

∙ 12/23/2020

Probabilistic Iterative Methods for Linear Systems

This paper presents a probabilistic perspective on iterative methods for...

0 Jon Cockayne, et al. ∙

research

∙ 10/22/2020

Measure Transport with Kernel Stein Discrepancy

Measure transport underpins several recent algorithms for posterior appr...

6 Matthew A Fisher, et al. ∙

research

∙ 10/16/2020

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks

Bayesian neural networks attempt to combine the strong predictive perfor...

0 Takuo Matsubara, et al. ∙

research

∙ 10/14/2020

Optimal quantisation of probability measures using maximum mean discrepancy

Several researchers have proposed minimisation of maximum mean discrepan...

0 Onur Teymur, et al. ∙

research

∙ 08/07/2020

A Probabilistic Numerical Extension of the Conjugate Gradient Method

We present a Conjugate Gradient (CG) implementation of the probabilistic...

0 Tim W. Reid, et al. ∙

research

∙ 06/12/2020

Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization

Control variates are a well-established tool to reduce the variance of M...

0 Shijing Si, et al. ∙

research

∙ 05/08/2020

Optimal Thinning of MCMC Output

The use of heuristics to assess the convergence and compress the output ...

0 Marina Riabiz, et al. ∙

research

∙ 04/27/2020

Integration in reproducing kernel Hilbert spaces of Gaussian kernels

The Gaussian kernel plays a central role in machine learning, uncertaint...

0 Toni Karvonen, et al. ∙

research

∙ 01/31/2020

Semi-Exact Control Functionals From Sard's Method

This paper focuses on the numerical computation of posterior expected qu...

0 Leah F. South, et al. ∙

research

∙ 01/29/2020

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

Despite the ubiquity of the Gaussian process regression model, few theor...

20 Toni Karvonen, et al. ∙

research

∙ 12/22/2019

Discussion of "Unbiased Markov chain Monte Carlo with couplings" by Pierre E. Jacob, John O'Leary and Yves F. Atchadé

This is a contribution for the discussion on "Unbiased Markov chain Mont...

0 Leah F. South, et al. ∙

research

∙ 10/07/2019

A Locally Adaptive Bayesian Cubature Method

Bayesian cubature (BC) is a popular inferential perspective on the cubat...

0 Matthew A Fisher, et al. ∙

research

∙ 06/24/2019

A Role for Symmetry in the Bayesian Solution of Differential Equations

The interpretation of numerical methods, such as finite difference metho...

0 Junyang Wang, et al. ∙

research

∙ 05/09/2019

Stein Point Markov Chain Monte Carlo

An important task in machine learning and statistics is the approximatio...

12 Wilson Ye Chen, et al. ∙

research

∙ 01/14/2019

Optimality Criteria for Probabilistic Numerical Methods

It is well understood that Bayesian decision theory and average case ana...

0 Chris J. Oates, et al. ∙

research

∙ 11/28/2018

Improved Calibration of Numerical Integration Error in Sigma-Point Filters

The sigma-point filters, such as the UKF, which exploit numerical quadra...

10 Jakub Prüher, et al. ∙

research

∙ 11/26/2018

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"

This article is the rejoinder for the paper "Probabilistic Integration: ...

18 Francois-Xavier Briol, et al. ∙

research

∙ 11/13/2018

Regularised Zero-Variance Control Variates for High-Dimensional Variance Reduction

Zero-variance control variates (ZV-CV) are a post-processing method to r...

0 Leah F. South, et al. ∙

research

∙ 10/11/2018

A Riemannian-Stein Kernel Method

This paper presents a theoretical analysis of numerical integration base...

0 Alessandro Barp, et al. ∙

research

∙ 09/26/2018

Symmetry Exploits for Bayesian Cubature Methods

Bayesian cubature provides a flexible framework for numerical integratio...

0 Toni Karvonen, et al. ∙

research

∙ 04/09/2018

A Bayes-Sard Cubature Method

This paper focusses on the formulation of numerical integration as an in...

0 Toni Karvonen, et al. ∙

research

∙ 03/27/2018

Stein Points

An important task in computational statistics and machine learning is to...

0 Wilson Ye Chen, et al. ∙

research

∙ 12/05/2017

Posterior Integration on a Riemannian Manifold

The geodesic Markov chain Monte Carlo method and its variants enable com...

0 Chris J. Oates, et al. ∙

research

∙ 12/05/2017

Posterior Integration on an Embedded Riemannian Manifold

This note extends the posterior integration method of Oates et al. (2016...

0 Chris J. Oates, et al. ∙

research

∙ 06/11/2017

On the Sampling Problem for Kernel Quadrature

The standard Kernel Quadrature method for numerical integration with ran...

0 Francois-Xavier Briol, et al. ∙

research

∙ 12/03/2015

Probabilistic Integration: A Role in Statistical Computation?

A research frontier has emerged in scientific computation, wherein numer...

0 Francois-Xavier Briol, et al. ∙

research

∙ 06/08/2015

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

There is renewed interest in formulating integration as an inference pro...

0 Francois-Xavier Briol, et al. ∙

research

∙ 04/04/2014

Exact Estimation of Multiple Directed Acyclic Graphs

This paper considers the problem of estimating the structure of multiple...

0 Chris J. Oates, et al. ∙

Chris J. Oates

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