David R. Burt

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 24

Top Topics

Gaussian Process

Top Venues

Publications

David R. Burt, Yunyi Shen, Tamara Broderick
Consistent Validation for Predictive Methods in Spatial Settings. CoRR (2024)
Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. ICML (2023)
Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. CoRR (2023)
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS (2022)
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR (2022)
Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
Wide Mean-Field Bayesian Neural Networks Ignore the Data. CoRR (2022)
Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR (2022)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt
A Note on the Chernoff Bound for Random Variables in the Unit Interval. CoRR (2022)
Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
Wide Mean-Field Bayesian Neural Networks Ignore the Data. AISTATS (2022)
David R. Burt, Artem Artemev, Mark van der Wilk
Barely Biased Learning for Gaussian Process Regression. CoRR (2021)
Artem Artemev, David R. Burt, Mark van der Wilk
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. CoRR (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
How Tight Can PAC-Bayes be in the Small Data Regime? CoRR (2021)
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS (2021)
Artem Artemev, David R. Burt, Mark van der Wilk
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. ICML (2021)
David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk
Understanding Variational Inference in Function-Space. CoRR (2020)
David Janz, David R. Burt, Javier Gonzalez
Bandit optimisation of functions in the Matérn kernel RKHS. AISTATS (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR (2020)
David Janz, David R. Burt, Javier González
Bandit optimisation of functions in the Matérn kernel RKHS. CoRR (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. 21 (2020)
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Variational Orthogonal Features. CoRR (2020)
David R. Burt, Carl E. Rasmussen, Mark van der Wilk
Rates of Convergence for Sparse Variational Gaussian Process Regression. CoRR (2019)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML (2019)
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR (2019)