4 citations · 4 across the 9 of their papers we have counts for
9 papers
Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning
Disha Hegde, Jon Cockayne, Chris. J. Oates
Rapid prototyping of algorithms is a critical step in modern machine learning. Most algorithms exploit linear algebra, creating a need for lightweight numerical routines which -- w…
Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
Disha Hegde, Marvin Pförtner, Jon Cockayne
Probabilistic linear solvers (PLSs) return probability distributions that quantify uncertainty due to limited computation in the solution of linear systems. The literature has trad…
Constructive Disintegration and Conditional Modes
Nathaël Da Costa, Marvin Pförtner, Jon Cockayne
Conditioning, the central operation in Bayesian statistics, is formalised by the notion of disintegration of measures. However, due to the implicit nature of their definition, cons…
Randomised Postiterations for Calibrated BayesCG
Niall Vyas, Disha Hegde, Jon Cockayne
The Bayesian conjugate gradient method offers probabilistic solutions to linear systems but suffers from poor calibration, limiting its utility in uncertainty quantification tasks.…
Learning to Solve Related Linear Systems
Disha Hegde, Jon Cockayne
Solving multiple parametrised related systems is an essential component of many numerical tasks, and learning from the already solved systems will make this process faster. In this…
Calibrated Computation-Aware Gaussian Processes
Disha Hegde, Mohamed Adil, Jon Cockayne
Gaussian processes are notorious for scaling cubically with the size of the training set, preventing application to very large regression problems. Computation-aware Gaussian proce…
Computation-Aware Kalman Filtering and Smoothing
Marvin Pförtner, Jonathan Wenger, Jon Cockayne +1
Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with…
Radiative Transfer as a Bayesian Linear Regression problem
Frederik De Ceuster, Thomas Ceulemans, Jon Cockayne +2
Electromagnetic radiation plays a crucial role in various physical and chemical processes. Hence, almost all astrophysical simulations require some form of radiative transfer model…
Comments on "Bayesian Solution Uncertainty Quantification for Differential Equations" by Chkrebtii, Campbell, Calderhead & Girolami
Francois-Xavier Briol, Jon Cockayne, Onur Teymur
We commend the authors for an exciting paper which provides a strong contribution to the emerging field of probabilistic numerics (PN). Below, we discuss aspects of prior modelling…