David R. Burt

According to our database, David R. Burt authored at least 18 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees.

Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

J. Mach. Learn. Res., 2024

A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making.

Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick

CoRR, 2024

Consistent Validation for Predictive Methods in Spatial Settings.

David R. Burt, Yunyi Shen, Tamara Broderick

CoRR, 2024

2023

Gaussian processes at the Helm(holtz): A more fluid model for ocean currents.

Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick

Proceedings of the International Conference on Machine Learning, 2023

2022

A Note on the Chernoff Bound for Random Variables in the Unit Interval.

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt

CoRR, 2022

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Wide Mean-Field Bayesian Neural Networks Ignore the Data.

Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Barely Biased Learning for Gaussian Process Regression.

David R. Burt, Artem Artemev, Mark van der Wilk

CoRR, 2021

How Tight Can PAC-Bayes be in the Small Data Regime?

Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients.

Artem Artemev, David R. Burt, Mark van der Wilk

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Convergence of Sparse Variational Inference in Gaussian Processes Regression.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

J. Mach. Learn. Res., 2020

Understanding Variational Inference in Function-Space.

David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk

CoRR, 2020

Variational Orthogonal Features.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

CoRR, 2020

Bandit optimisation of functions in the Matérn kernel RKHS.

David Janz, David R. Burt, Javier González

CoRR, 2020

On the Expressiveness of Approximate Inference in Bayesian Neural Networks.

Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Bandit optimisation of functions in the Matérn kernel RKHS.

David Janz, David R. Burt, Javier Gonzalez

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks.

Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner

CoRR, 2019

Rates of Convergence for Sparse Variational Gaussian Process Regression.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

Proceedings of the 36th International Conference on Machine Learning, 2019