Burt, David R.

12 publications

TMLR 2025 Approximations to Worst-Case Data Dropping: Unmasking Failure Modes Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick

ICLRW 2025 Approximations to Worst-Case Data Dropping: Unmasking Failure Modes Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick

AISTATS 2025 Consistent Validation for Predictive Methods in Spatial Settings David R. Burt, Yunyi Shen, Tamara Broderick

NeurIPS 2025 Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associations David R. Burt, Renato Berlinghieri, Stephen Bates, Tamara Broderick

ICMLW 2024 Consistent Validation for Predictive Methods in Spatial Settings David R. Burt, Yunyi Shen, Tamara Broderick

JMLR 2024 Numerically Stable Sparse Gaussian Processes via Minimum Separation Using Cover Trees Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

ICML 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 Özgökmen, Junfei Xia, Tamara Broderick

ICLRW 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 Özgökmen, Junfei Zia, Tamara Broderick

AISTATS 2022 Wide Mean-Field Bayesian Neural Networks Ignore the Data Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez

NeurIPSW 2021 Barely Biased Learning for Gaussian Process Regression David R. Burt, Artem Artemev, Mark van der Wilk

ICML 2021 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients Artem Artemev, David R. Burt, Mark Wilk

JMLR 2020 Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt, Carl Edward Rasmussen, Mark van der Wilk