87 citations · 415 across the 70 of their papers we have counts for
78 papers
An Unscented PrO Kalman Filter
Dan Kean, Zheyang Shen, Chris. J. Oates
Predictively-oriented (PrO) filtering for state-space models was proposed in Shen et al. [2026] as a promising route to avoiding over-confident inferences in settings where either…
Overcoming Model Misspecification in Bayesian Inference of Molecular Signalling Networks
Yinghan Li, Heishiro Kanagawa, Francesca Romana Crucinio +2
Bayesian inference of molecular signalling networks usually relies on tractability of the marginal likelihood, enabling the set of possible networks to be efficiently explored. As…
A Distributional Optimisation Perspective on Combining Models in Deep Learning
Congye Wang, Yan Lin, Zheyang Shen +2
Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule used to combine them are typic…
Why not to use the Gaussian kernel
Toni Karvonen, Chris J. Oates
Kernels measure similarity or correlation in tasks such as regression and classification. The Gaussian kernel, other names of which include squared exponential and radial basis fun…
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…
Predictively-Oriented Kalman Filtering
Zheyang Shen, Gerardo Duran-Martin, Chris. J. Oates
This paper presents a post-Bayesian approach to online filtering in nonlinear state-space models, capable of avoiding over-confident inferences in settings where either the dynamic…