Rates of Convergence for Sparse Variational Gaussian Process Regression. - David R. Burt; Carl Edward Rasmussen; Mark van der Wilk (ICML, 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)

Bandit optimisation of functions in the Matérn kernel RKHS. - David Janz; David R. Burt; Javier Gonzalez (AISTATS, 2020)

On the Expressiveness of Approximate Inference in Bayesian Neural Networks. - Andrew Y. K. Foong; David R. Burt; Yingzhen Li; Richard E. Turner (NeurIPS, 2020)

Bandit optimisation of functions in the Matérn kernel RKHS. - David Janz; David R. Burt; Javier González 002 (CoRR, 2020)

Variational Orthogonal Features. - David R. Burt; Carl Edward Rasmussen; Mark van der Wilk (CoRR, 2020)

Understanding Variational Inference in Function-Space. - David R. Burt; Sebastian W. Ober; Adrià Garriga-Alonso; Mark van der Wilk (CoRR, 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)

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. - Artem Artemev; David R. Burt; Mark van der Wilk (ICML, 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 (NeurIPS, 2021)

Barely Biased Learning for Gaussian Process Regression. - David R. Burt; Artem Artemev; Mark van der Wilk (CoRR, 2021)

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

Sparse Gaussian Process Hyperparameters: Optimize or Integrate? - Vidhi Lalchand; Wessel P. Bruinsma; David R. Burt; Carl Edward Rasmussen (NeurIPS, 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)

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 (ICML, 2023)

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)

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)

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

Approximations to worst-case data dropping: unmasking failure modes. - Jenny Y. Huang; David R. Burt; Yunyi Shen; Tin D. Nguyen; Tamara Broderick (Trans. Mach. Learn. Res., 2025)

Lipschitz-Driven Inference: Bias-corrected Confidence Intervals for Spatial Linear Models. - David R. Burt; Renato Berlinghieri; Stephen Bates; Tamara Broderick (CoRR, 2025)