| Scalable Global Optimization via Local Bayesian Optimization D Eriksson, M Pearce, J Gardner, RD Turner, M Poloczek Advances in Neural Information Processing Systems, 2019 | 218 | 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 | 114 | 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 | 81 | 2017 |
| Scaling Gaussian Process Regression with Derivatives D Eriksson, K Dong, EH Lee, D Bindel, AG Wilson Advances in Neural Information Processing Systems, 2018 | 73 | 2018 |
| pySOT and POAP: An Event-Driven Asynchronous Framework for Surrogate Optimization D Eriksson, D Bindel, CA Shoemaker arXiv preprint arXiv:1908.00420, 2019 | 57 | 2019 |
| Scalable Constrained Bayesian Optimization D Eriksson, M Poloczek International Conference on Artificial Intelligence and Statistics, 730-738, 2021 | 37 | 2021 |
| Continental hydrology loading observed by VLBI measurements D Eriksson, DS MacMillan Journal of Geodesy 88 (7), 675-690, 2014 | 36 | 2014 |
| High-dimensional Bayesian optimization with sparse axis-aligned subspaces D Eriksson, M Jankowiak Uncertainty in Artificial Intelligence, 493-503, 2021 | 30 | 2021 |
| 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 | 30 | 2014 |
| Multi-objective bayesian optimization over high-dimensional search spaces S Daulton, D Eriksson, M Balandat, E Bakshy Uncertainty in Artificial Intelligence, 507-517, 2022 | 27 | 2022 |
| 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 | 27 | 2020 |
| Surrogate optimization toolbox (pySOT) D Eriksson, D Bindel, C Shoemaker github.com/dme65/pySOT, 2015 | 12 | 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 | 10 | 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 | 7 | 2021 |
| A nonmyopic approach to cost-constrained Bayesian optimization EH Lee, D Eriksson, V Perrone, M Seeger Uncertainty in Artificial Intelligence, 568-577, 2021 | 6 | 2021 |
| Approximate distance queries for path-planning in massive point clouds D Eriksson, E Shellshear 2014 11th International Conference on Informatics in Control, Automation and …, 2014 | 4 | 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 | 3 | 2022 |
| Sparse bayesian optimization S Liu, Q Feng, D Eriksson, B Letham, E Bakshy arXiv preprint arXiv:2203.01900, 2022 | 2 | 2022 |
| Fast exact shortest distance queries for massive point clouds D Eriksson, E Shellshear Graphical Models 84, 28-37, 2016 | 1 | 2016 |
| Point cloud simplification and processing for path-planning D ERIKSSON Master’s thesis, Chalmers University of Technology, 2014 | 1 | 2014 |
Matthias PoloczekPrincipal Scientist at AmazonVerified email at cornell.edu
