4 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…