David R. Burt - RankMe

30 papers A* 6A 3Journal 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Approximations to worst-case data dropping: unmasking failure modes. Trans. Mach. Learn. Res. Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick
2025	A	`conf`	Consistent Validation for Predictive Methods in Spatial Settings. AISTATS David R. Burt, Yunyi Shen, Tamara Broderick
2025	J	`jnl`	Lipschitz-Driven Inference: Bias-corrected Confidence Intervals for Spatial Linear Models. CoRR David R. Burt, Renato Berlinghieri, Stephen Bates, Tamara Broderick
2025	J	`jnl`	Wrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification. CoRR David R. Burt, Renato Berlinghieri, Tamara Broderick
2024	J	`jnl`	A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making. CoRR Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick
2024	J	`jnl`	Consistent Validation for Predictive Methods in Spatial Settings. CoRR David R. Burt, Yunyi Shen, Tamara Broderick
2024	J	`jnl`	Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
2023	A*	`conf`	Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. ICML Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan James Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick
2023	J	`jnl`	Gaussian processes at the Helm(holtz): A more fluid model for ocean currents. CoRR Renato Berlinghieri, Brian L. Trippe, David R. Burt, Ryan Giordano, Kaushik Srinivasan, Tamay M. Özgökmen, Junfei Xia, Tamara Broderick
2022	J	`jnl`	A Note on the Chernoff Bound for Random Variables in the Unit Interval. CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt
2022	J	`jnl`	Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
2022	A*	`conf`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
2022	J	`jnl`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen
2022	A	`conf`	Wide Mean-Field Bayesian Neural Networks Ignore the Data. AISTATS Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
2022	J	`jnl`	Wide Mean-Field Bayesian Neural Networks Ignore the Data. CoRR Beau Coker, Wessel P. Bruinsma, David R. Burt, Weiwei Pan, Finale Doshi-Velez
2021	J	`jnl`	Barely Biased Learning for Gaussian Process Regression. CoRR David R. Burt, Artem Artemev, Mark van der Wilk
2021	A*	`conf`	How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
2021	J	`jnl`	How Tight Can PAC-Bayes be in the Small Data Regime? CoRR Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner
2021	A*	`conf`	Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. ICML Artem Artemev, David R. Burt, Mark van der Wilk
2021	J	`jnl`	Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. CoRR Artem Artemev, David R. Burt, Mark van der Wilk
2020	A	`conf`	Bandit optimisation of functions in the Matérn kernel RKHS. AISTATS David Janz, David R. Burt, Javier González
2020	J	`jnl`	Bandit optimisation of functions in the Matérn kernel RKHS. CoRR David Janz, David R. Burt, Javier González
2020	J	`jnl`	Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2020	J	`jnl`	Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2020	A*	`conf`	On the Expressiveness of Approximate Inference in Bayesian Neural Networks. NeurIPS Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
2020	J	`jnl`	Understanding Variational Inference in Function-Space. CoRR David R. Burt, Sebastian W. Ober, Adrià Garriga-Alonso, Mark van der Wilk
2020	J	`jnl`	Variational Orthogonal Features. CoRR David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2019	J	`jnl`	Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks. CoRR Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner
2019	A*	`conf`	Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2019	J	`jnl`	Rates of Convergence for Sparse Variational Gaussian Process Regression. CoRR David R. Burt, Carl E. Rasmussen, Mark van der Wilk