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Samuel (Sam) Daulton
Samuel (Sam) Daulton
Research Scientist - Meta, University of Oxford
Verified email at fb.com - Homepage
Title
Cited by
Cited by
Year
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
251*2020
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
902017
Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization
S Daulton, M Balandat, E Bakshy
Advances in Neural Information Processing Systems 33, 2020
612020
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
132021
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
122019
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
102021
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
62022
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
32021
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
H Namkoong, S Daulton, E Bakshy
NeurIPS Offline RL Workshop, 2020
22020
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
2022
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