Oates, Chris J.

17 publications

AISTATS 2025 Prediction-Centric Uncertainty Quantification via MMD Zheyang Shen, Jeremias Knoblauch, Samuel Power, Chris J. Oates
AISTATS 2025 Reinforcement Learning for Adaptive MCMC Congye Wang, Wilson Ye Chen, Heishiro Kanagawa, Chris J. Oates
NeurIPS 2023 Gradient-Free Kernel Stein Discrepancy Matthew Fisher, Chris J Oates
JMLR 2023 Maximum Likelihood Estimation in Gaussian Process Regression Is Ill-Posed Toni Karvonen, Chris J. Oates
UAI 2023 Meta-Learning Control Variates: Variance Reduction with Limited Data Zhuo Sun, Chris J Oates, François-Xavier Briol
TMLR 2023 Sobolev Spaces, Kernels and Discrepancies over Hyperspheres Simon Hubbert, Emilio Porcu, Chris J. Oates, Mark Girolami
NeurIPS 2023 Stein $\Pi$-Importance Sampling Congye Wang, Ye Chen, Heishiro Kanagawa, Chris J Oates
JMLR 2022 Testing Whether a Learning Procedure Is Calibrated Jon Cockayne, Matthew M. Graham, Chris J. Oates, T. J. Sullivan, Onur Teymur
NeurIPS 2021 Black Box Probabilistic Numerics Onur Teymur, Christopher Foley, Philip Breen, Toni Karvonen, Chris J Oates
JMLR 2021 Probabilistic Iterative Methods for Linear Systems Jon Cockayne, Ilse C.F. Ipsen, Chris J. Oates, Tim W. Reid
JMLR 2021 The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks Takuo Matsubara, Chris J. Oates, François-Xavier Briol
JMLR 2019 Causal Learning via Manifold Regularization Steven M. Hill, Chris J. Oates, Duncan A. Blythe, Sach Mukherjee
NeurIPS 2018 A Bayes-Sard Cubature Method Toni Karvonen, Chris J Oates, Simo Sarkka
ICML 2017 On the Sampling Problem for Kernel Quadrature François-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami
AISTATS 2016 Control Functionals for Quasi-Monte Carlo Integration Chris J. Oates, Mark A. Girolami
JMLR 2016 Estimating Causal Structure Using Conditional DAG Models Chris. J. Oates, Jim Q. Smith, Sach Mukherjee
AISTATS 2014 Joint Structure Learning of Multiple Non-Exchangeable Networks Chris J. Oates, Sach Mukherjee