
Black Box Probabilistic Numerics
Probabilistic numerics casts numerical tasks, such the numerical solutio...
read it

Stein's Method Meets Statistics: A Review of Some Recent Developments
Stein's method is a collection of tools for analysing distributional com...
read it

Bayesian Numerical Methods for Nonlinear Partial Differential Equations
The numerical solution of differential equations can be formulated as an...
read it

Robust Generalised Bayesian Inference for Intractable Likelihoods
Generalised Bayesian inference updates prior beliefs using a loss functi...
read it

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

Testing whether a Learning Procedure is Calibrated
A learning procedure takes as input a dataset and performs inference for...
read it

Probabilistic Iterative Methods for Linear Systems
This paper presents a probabilistic perspective on iterative methods for...
read it

Measure Transport with Kernel Stein Discrepancy
Measure transport underpins several recent algorithms for posterior appr...
read it

The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks
Bayesian neural networks attempt to combine the strong predictive perfor...
read it

Optimal quantisation of probability measures using maximum mean discrepancy
Several researchers have proposed minimisation of maximum mean discrepan...
read it

A Probabilistic Numerical Extension of the Conjugate Gradient Method
We present a Conjugate Gradient (CG) implementation of the probabilistic...
read it

Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Control variates are a wellestablished tool to reduce the variance of M...
read it

Optimal Thinning of MCMC Output
The use of heuristics to assess the convergence and compress the output ...
read it

Integration in reproducing kernel Hilbert spaces of Gaussian kernels
The Gaussian kernel plays a central role in machine learning, uncertaint...
read it

SemiExact Control Functionals From Sard's Method
This paper focuses on the numerical computation of posterior expected qu...
read it

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Despite the ubiquity of the Gaussian process regression model, few theor...
read it

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...
read it

A Locally Adaptive Bayesian Cubature Method
Bayesian cubature (BC) is a popular inferential perspective on the cubat...
read it

A Role for Symmetry in the Bayesian Solution of Differential Equations
The interpretation of numerical methods, such as finite difference metho...
read it

Stein Point Markov Chain Monte Carlo
An important task in machine learning and statistics is the approximatio...
read it

Optimality Criteria for Probabilistic Numerical Methods
It is well understood that Bayesian decision theory and average case ana...
read it

Improved Calibration of Numerical Integration Error in SigmaPoint Filters
The sigmapoint filters, such as the UKF, which exploit numerical quadra...
read it

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
read it

Regularised ZeroVariance Control Variates for HighDimensional Variance Reduction
Zerovariance control variates (ZVCV) are a postprocessing method to r...
read it

A RiemannianStein Kernel Method
This paper presents a theoretical analysis of numerical integration base...
read it

Symmetry Exploits for Bayesian Cubature Methods
Bayesian cubature provides a flexible framework for numerical integratio...
read it

A BayesSard Cubature Method
This paper focusses on the formulation of numerical integration as an in...
read it

Stein Points
An important task in computational statistics and machine learning is to...
read it

Posterior Integration on a Riemannian Manifold
The geodesic Markov chain Monte Carlo method and its variants enable com...
read it

Posterior Integration on an Embedded Riemannian Manifold
This note extends the posterior integration method of Oates et al. (2016...
read it

On the Sampling Problem for Kernel Quadrature
The standard Kernel Quadrature method for numerical integration with ran...
read it

Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numer...
read it

FrankWolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
There is renewed interest in formulating integration as an inference pro...
read it

Exact Estimation of Multiple Directed Acyclic Graphs
This paper considers the problem of estimating the structure of multiple...
read it
Chris J. Oates
is this you? claim profile