David Eriksson
David Eriksson
Staff Research Scientist at Meta
Verified email at - Homepage
Cited by
Cited by
Scalable Global Optimization via Local Bayesian Optimization
D Eriksson, M Pearce, J Gardner, RD Turner, M Poloczek
Advances in Neural Information Processing Systems, 2019
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
R Turner, D Eriksson, M McCourt, J Kiili, E Laaksonen, Z Xu, I Guyon
NeurIPS 2020 Competition and Demonstration Track, 3-26, 2021
Scalable Log Determinants for Gaussian Process Kernel Learning
K Dong, D Eriksson, H Nickisch, D Bindel, AG Wilson
Advances in Neural Information Processing Systems, 2017
Scaling Gaussian Process Regression with Derivatives
D Eriksson, K Dong, EH Lee, D Bindel, AG Wilson
Advances in Neural Information Processing Systems, 2018
pySOT and POAP: An Event-Driven Asynchronous Framework for Surrogate Optimization
D Eriksson, D Bindel, CA Shoemaker
arXiv preprint arXiv:1908.00420, 2019
Scalable Constrained Bayesian Optimization
D Eriksson, M Poloczek
International Conference on Artificial Intelligence and Statistics, 730-738, 2021
Continental hydrology loading observed by VLBI measurements
D Eriksson, DS MacMillan
Journal of Geodesy 88 (7), 675-690, 2014
Tropospheric delay ray tracing applied in VLBI analysis
D Eriksson, DS MacMillan, JM Gipson
Journal of Geophysical Research: Solid Earth 119 (12), 9156-9170, 2014
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
G Pleiss, M Jankowiak, D Eriksson, A Damle, JR Gardner
Advances in Neural Information Processing Systems, 2020
High-dimensional Bayesian optimization with sparse axis-aligned subspaces
D Eriksson, M Jankowiak
Uncertainty in Artificial Intelligence, 493-503, 2021
Multi-objective bayesian optimization over high-dimensional search spaces
S Daulton, D Eriksson, M Balandat, E Bakshy
Uncertainty in Artificial Intelligence, 507-517, 2022
Surrogate optimization toolbox (pySOT)
D Eriksson, D Bindel, C Shoemaker, 2015
Efficient rollout strategies for Bayesian optimization
E Lee, D Eriksson, D Bindel, B Cheng, M Mccourt
Conference on Uncertainty in Artificial Intelligence, 260-269, 2020
Latency-aware neural architecture search with multi-objective bayesian optimization
D Eriksson, PIJ Chuang, S Daulton, P Xia, A Shrivastava, A Babu, S Zhao, ...
arXiv preprint arXiv:2106.11890, 2021
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
EH Lee, D Eriksson, V Perrone, M Seeger
Uncertainty in Artificial Intelligence, 568-577, 2021
Approximate distance queries for path-planning in massive point clouds
D Eriksson, E Shellshear
2014 11th International Conference on Informatics in Control, Automation andá…, 2014
Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization
S Daulton, X Wan, D Eriksson, M Balandat, MA Osborne, E Bakshy
arXiv preprint arXiv:2210.10199, 2022
Sparse Bayesian Optimization
S Liu, Q Feng, D Eriksson, B Letham, E Bakshy
arXiv preprint arXiv:2203.01900, 2022
Fast exact shortest distance queries for massive point clouds
D Eriksson, E Shellshear
Graphical Models 84, 28-37, 2016
Point cloud simplification and processing for path-planning
D Eriksson
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