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Daniel R. Jiang
Daniel R. Jiang
Otros nombresDaniel Jiang
Meta & University of Pittsburgh
Dirección de correo verificada de meta.com - Página principal
Título
Citado por
Citado por
Año
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, 2020
850*2020
Optimal hour ahead bidding in the real time electricity market
DR Jiang, WB Powell
INFORMS Journal on Computing 27 (3), 525-543, 2015
1182015
An approximate dynamic programming algorithm for monotone value functions
DR Jiang, WB Powell
Operations Research 63 (6), 1489-1511, 2015
922015
A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work?
DR Jiang, TV Pham, WB Powell, DF Salas, WR Scott
2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement …, 2014
672014
Risk-averse approximate dynamic programming with quantile-based risk measures
DR Jiang, WB Powell
Mathematics of Operations Research 43 (2), 554-579, 2018
54*2018
Efficient nonmyopic Bayesian optimization via one-shot multi-step trees
S Jiang, DR Jiang, M Balandat, B Karrer, JR Gardner, R Garnett
Advances in Neural Information Processing Systems 33, 2020
482020
Dynamic inventory repositioning in on-demand rental networks
S Benjaafar, D Jiang, X Li, X Li
Management Science 68 (11), 7793-8514, 2022
47*2022
Feedback-based tree search for reinforcement learning
DR Jiang, E Ekwedike, H Liu
International Conference on Machine Learning (ICML), 2284-2293, 2018
372018
Shape constraints in economics and operations research
AL Johnson, DR Jiang
Statistical Science 33 (4), 527-546, 2018
202018
Practicality of nested risk measures for dynamic electric vehicle charging
DR Jiang, WB Powell
https://arxiv.org/abs/1605.02848, 2017
19*2017
Optimistic Monte Carlo tree search with sampled information relaxation dual bounds
DR Jiang, L Al-Kanj, WB Powell
Operations Research 68 (6), 1678-1697, 2020
17*2020
Multi-step budgeted Bayesian optimization with unknown evaluation costs
R Astudillo, D Jiang, M Balandat, E Bakshy, P Frazier
Advances in Neural Information Processing Systems 34, 2021
162021
Lookahead-bounded Q-learning
IE Shar, DR Jiang
International Conference on Machine Learning (ICML), 2020
11*2020
Interpretable personalized experimentation
H Wu, S Tan, W Li, M Garrard, A Obeng, D Dimmery, S Singh, H Wang, ...
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
9*2021
Weakly coupled deep Q-networks
IE Shar, DR Jiang
Advances in Neural Information Processing Systems 37, 2023
32023
On noisy evaluation in federated hyperparameter tuning
K Kuo, P Thaker, M Khodak, J Nguyen, D Jiang, A Talwalkar, V Smith
Proceedings of Machine Learning and Systems 5, 2023
32023
Pearl: A production-ready reinforcement learning agent
Z Zhu, RS Braz, J Bhandari, D Jiang, Y Wan, Y Efroni, L Wang, R Xu, ...
arXiv preprint arXiv:2312.03814, 2023
22023
Dynamic subgoal-based exploration via Bayesian optimization
Y Wang, M Poloczek, DR Jiang
Transactions on Machine Learning Research, 2023
1*2023
Towards green, accurate, and efficient AI models through multi-objective optimization
U Gupta, D Jiang, M Balandat, CJ Wu
ICLR 2023 Workshop on Tackling Climate Change with Machine Learning, 2023
12023
Structured actor-critic for managing public health points-of-dispensing
Y Wang, DR Jiang
arXiv preprint arXiv:1806.02490, 2019
12019
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Artículos 1–20