Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
Published in Preprint, 2025
David R. Burt, Renato Berlinghieri, Tamara Broderick Arxiv
Seleteced Publications
Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
Published in Neural Information Processing Systems (NeurIPS), 2025
David R. Burt*, Renato Berlinghieri*, Stephen Bates, Tamara Broderick Arxiv Code
Consistent Validation for Predictive Methods in Spatial Settings
Published in Artificial Intelligence and Statistics (AISTATS), 2025
David R. Burt, Yunyi Shen, Tamara Broderick
Are Hourly PM2.5 Forecasts Sufficiently Accurate to Plan Your Day? Individual Decision Making in the Face of Increasing Wildfire Smoke
Published in Preprint, 2024
Renato Berlinghieri*, David R. Burt*, Paolo Giani, Arlene M. Fiore, Tamara Broderick Arxiv Code
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
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
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
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
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
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
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
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