Rates of Convergence for Sparse Variational Gaussian Process Regression
Published in International Conference on Machine Learning (ICML), 2019
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk Proceedings Arxiv Code
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Bandit optimisation of functions in the Matérn kernel RKHS
Published in Artificial Intelligence and Statistics (AISTATS), 2020
David Janz, David R. Burt, Javier González Proceedings Arxiv Code
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Published in Journal of Machine Learning Research, 2020
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk Article Arxiv Code
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Published in Neural Information Processing Systems (NeurIPS), 2020
Andrew Y.K. Foong*, David R. Burt*, Yingzhen Li, Richard Turner
Proceedings Arxiv Code Slides
How Tight Can PAC-Bayes be in the Small Data Regime?
Published in Neural Information Processing Systems (NeurIPS), 2021
Andrew Y.K. Foong*, Wessel Bruinsma*, David R. Burt, Richard E. Turner
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Published in International Conference on Machine Learning (ICML), 2021
Artem Artemev*, David R. Burt*, Mark van der Wilk
Proceedings Arxiv Slides
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Published in Artificial Intelligence and Statistics (AISTATS), 2022
Beau Coker*, Wessel Bruinsma*, David R. Burt*, Weiwei Pan, Finale Doshi-Velez Arxiv
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
Published in International Conference on Machine Learning (ICML), 2023
Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay Özgökmen, Junfei Xia, Tamara Broderick Arxiv