| Scalable global optimization via local Bayesian optimization D Eriksson, M Pearce, J Gardner, RD Turner, M Poloczek Advances in neural information processing systems 32, 2019 | 592 | 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 | 426 | 2021 |
| High-dimensional Bayesian optimization with sparse axis-aligned subspaces D Eriksson, M Jankowiak Uncertainty in Artificial Intelligence, 493-503, 2021 | 171 | 2021 |
| Scalable constrained Bayesian optimization D Eriksson, M Poloczek International conference on artificial intelligence and statistics, 730-738, 2021 | 139 | 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 | 126 | 2022 |
| Scalable log determinants for Gaussian process kernel learning K Dong, D Eriksson, H Nickisch, D Bindel, AG Wilson Advances in Neural Information Processing Systems 30, 2017 | 114 | 2017 |
| Scaling Gaussian process regression with derivatives D Eriksson, K Dong, E Lee, D Bindel, AG Wilson Advances in neural information processing systems 31, 2018 | 105 | 2018 |
| pySOT and POAP: An event-driven asynchronous framework for surrogate optimization D Eriksson, D Bindel, CA Shoemaker arXiv preprint arXiv:1908.00420, 2019 | 94 | 2019 |
| Unexpected improvements to expected improvement for bayesian optimization S Ament, S Daulton, D Eriksson, M Balandat, E Bakshy Advances in Neural Information Processing Systems 36, 20577-20612, 2023 | 62 | 2023 |
| Fast matrix square roots with applications to Gaussian processes and Bayesian optimization G Pleiss, M Jankowiak, D Eriksson, A Damle, J Gardner Advances in neural information processing systems 33, 22268-22281, 2020 | 61 | 2020 |
| Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization S Daulton, X Wan, D Eriksson, M Balandat, MA Osborne, E Bakshy Advances in Neural Information Processing Systems 35, 12760-12774, 2022 | 51 | 2022 |
| Continental hydrology loading observed by VLBI measurements D Eriksson, DS MacMillan Journal of Geodesy 88 (7), 675-690, 2014 | 43 | 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 | 36 | 2014 |
| A nonmyopic approach to cost-constrained Bayesian optimization EH Lee, D Eriksson, V Perrone, M Seeger Uncertainty in Artificial Intelligence, 568-577, 2021 | 34 | 2021 |
| 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 | 29 | 2020 |
| Discovering many diverse solutions with Bayesian optimization N Maus, K Wu, D Eriksson, J Gardner arXiv preprint arXiv:2210.10953, 2022 | 24 | 2022 |
| 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 | 16 | 2021 |
| Bayesian optimization over high-dimensional combinatorial spaces via dictionary-based embeddings A Deshwal, S Ament, M Balandat, E Bakshy, JR Doppa, D Eriksson International Conference on Artificial Intelligence and Statistics, 7021-7039, 2023 | 15 | 2023 |
| Surrogate optimization toolbox (pysot) D Eriksson, D Bindel, C Shoemaker URL: github. com/dme65/pySOT, 2015 | 15 | 2015 |
| Sparse bayesian optimization S Liu, Q Feng, D Eriksson, B Letham, E Bakshy International Conference on Artificial Intelligence and Statistics, 3754-3774, 2023 | 11 | 2023 |
Matthias PoloczekPrincipal Scientist at Amazoncornell.edu의 이메일 확인됨