14 citations · 47 across the 16 of their papers we have counts for
6 papers · 1 filter
Probabilistic Richardson Extrapolation
Chris. J. Oates, Toni Karvonen, Aretha L. Teckentrup +2
For over a century, extrapolation methods have provided a powerful tool to improve the convergence order of a numerical method. However, these tools are not well-suited to modern c…
Meta-learning Control Variates: Variance Reduction with Limited Data
Zhuo Sun, Chris J. Oates, François-Xavier Briol
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples…
Discussion of: "A Bayesian information criterion for singular models"
N. Friel, J. P. McKeone, C. J. Oates +1
Contributed discussion to the paper of Drton and Plummer (2017), presented before the Royal Statistical Society on 5th October 2016.
Estimating causal structure using conditional DAG models
Chris J. Oates, Jim Q. Smith, Sach Mukherjee
This paper considers inference of causal structure in a class of graphical models called "conditional DAGs". These are directed acyclic graph (DAG) models with two kinds of variabl…
The Controlled Thermodynamic Integral for Bayesian Model Comparison
Chris J. Oates, Theodore Papamarkou, Mark Girolami
Bayesian model comparison relies upon the model evidence, yet for many models of interest the model evidence is unavailable in closed form and must be approximated. Many of the est…
Towards a Multi-Subject Analysis of Neural Connectivity
Chris J. Oates, Lilia Carneiro da Costa, Tom Nichols
Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected fr…