| BoTorch: A framework for efficient Monte-Carlo Bayesian optimization M Balandat, B Karrer, D Jiang, S Daulton, B Letham, AG Wilson, E Bakshy Advances in neural information processing systems 33, 21524-21538, 2020 | 1080* | 2020 |
| Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization S Daulton, M Balandat, E Bakshy Advances in Neural Information Processing Systems 33, 2020 | 319 | 2020 |
| Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement S Daulton, M Balandat, E Bakshy Advances in Neural Information Processing Systems 34, 2021 | 186 | 2021 |
| Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes TW Killian, S Daulton, G Konadaris, F Doshi-Velez Advances in Neural Information Processing Systems 30, 2017 | 132 | 2017 |
| Multi-objective bayesian optimization over high-dimensional search spaces S Daulton, D Eriksson, M Balandat, E Bakshy Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial …, 2022 | 125 | 2022 |
| Optimizing coverage and capacity in cellular networks using machine learning RM Dreifuerst, S Daulton, Y Qian, P Varkey, M Balandat, S Kasturia, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 87 | 2021 |
| 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 | 52 | 2023 |
| 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, 2022 | 50 | 2022 |
| Robust Multi-Objective Bayesian Optimization Under Input Noise S Daulton, S Cakmak, M Balandat, MA Osborne, E Zhou, E Bakshy Proceedings of the 39th International Conference on Machine Learning, 2022 | 38 | 2022 |
| Thompson sampling for contextual bandit problems with auxiliary safety constraints S Daulton, S Singh, V Avadhanula, D Dimmery, E Bakshy NeurIPS Workshop on Safety and Robustness in Decision Making, 2019 | 20 | 2019 |
| Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization D Eriksson, PIJ Chuang, S Daulton, A Aly, A Babu, A Shrivastava, P Xia, ... ICML AutoML Workshop, 2021 | 17 | 2021 |
| Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information S Daulton, M Balandat, E Bakshy Proceedings of the 40th International Conference on Machine Learning, 2023 | 14 | 2023 |
| Log-Linear-Time Gaussian Processes Using Binary Tree Kernels MK Cohen, S Daulton, MA Osborne Advances in Neural Information Processing Systems 35, 2022 | 8 | 2022 |
| Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning H Namkoong, S Daulton, E Bakshy NeurIPS Offline RL Workshop, 2020 | 8 | 2020 |
| Unexpected improvements to expected improvement for Bayesian optimization S Daulton, S Ament, D Eriksson, M Balandat, E Bakshy Proceedings of the 37th International Conference on Neural Information …, 2023 | 4 | 2023 |
| Bayesian optimization of function networks with partial evaluations P Buathong, J Wan, R Astudillo, S Daulton, M Balandat, PI Frazier arXiv preprint arXiv:2311.02146, 2023 | 2 | 2023 |
| Bayesian optimization in adverse scenarios S Daulton University of Oxford, 2023 | | 2023 |
Maximilian BalandatResearch Scientist Manager, MetaBestätigte E-Mail-Adresse bei meta.com
