Scalable Constrained Bayesian Optimization (To appear)
David Eriksson, Matthias Poloczek
Artificial Intelligence and Statistics (AISTATS 2021)
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
[Paper]
[Code]
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Neural Information Processing Systems (NeurIPS 2020)
Efficient Rollout Strategies for Bayesian Optimization
[Paper]
[Code]
Eric Hans Lee, David Eriksson, Bolong Cheng, Michael McCourt, David Bindel
Uncertainty in Artificial Intelligence (UAI 2020)
Scalable Global Optimization via Local Bayesian Optimization
[Paper]
[Code]
David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
Neural Information Processing Systems (NeurIPS 2019)
(Spotlight, acceptance rate = 3.0%)
Scaling Gaussian Process Regression with Derivatives [Paper]
David Eriksson, Kun Dong, Eric Lee, David Bindel, Andrew G. Wilson
Neural Information Processing Systems (NeurIPS 2018)
Scalable log determinants for Gaussian process kernel learning [Paper]
Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew G. Wilson
Neural Information Processing Systems (NeurIPS 2017)
Fast exact shortest distance queries for massive point clouds [Paper]
David Eriksson, Evan Shellshear
Graphical Models (2016)
Tropospheric delay ray tracing applied in VLBI analysis [Paper]
David Eriksson, Daniel S. MacMillan, John M. Gipson
Journal of Geophysical Research: Solid Earth (2014)
Continental hydrology loading observed by VLBI measurements [Paper]
David Eriksson, Daniel S. MacMillan
Journal of Geodesy (2014)