Daniel R. Jiang

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Eytan BakshyResearch Director, MetaVerified email at fb.com
Maximilian BalandatResearch Scientist Manager, MetaVerified email at meta.com
Warrren B PowellPrinceton UniversityVerified email at princeton.edu
Brian KarrerResearch Science Manager, MetaVerified email at fb.com
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Samuel DaultonResearch Scientist, MetaVerified email at meta.com
Xiaobo LiAssistant Professor of Industrial Engineering, National University of SingaporeVerified email at nus.edu.sg
Saif BenjaafarUniversity of MinnesotaVerified email at umn.edu
Ibrahim El SharMachine Learning Scientist, PhD at University of PittsburghVerified email at pitt.edu
Lina Al-KanjAssociate Research Scholar, Princeton UniversityVerified email at princeton.edu
Roman GarnettWashington University in St. LouisVerified email at wustl.edu
Shali JiangFacebookVerified email at wustl.edu
Jacob GardnerUniversity of PennsylvaniaVerified email at seas.upenn.edu
Peter I FrazierAssociate Professor of Operations Research and Information Engineering, Cornell UniversityVerified email at cornell.edu
Raul AstudilloPostdoctoral Scholar, California Institute of TechnologyVerified email at caltech.edu
Han LiuOrrington Lunt Professor of Computer Science, Statistics and Data Science, Northwestern UniversityVerified email at northwestern.edu
Emmanuel EkwedikeMachine Learning Research ScientistVerified email at princeton.edu
Shaun SinghResearch Engineer, FacebookVerified email at fb.com
Drew DimmeryHertie SchoolVerified email at hertie-school.org
Sarah TanSalesforce / Cornell UniversityVerified email at cornell.edu

Daniel R. Jiang

Other namesDaniel Jiang

Meta & University of Pittsburgh

Verified email at meta.com - Homepage

reinforcement learning Bayesian optimization sequential decision making


Title Sort by citations Sort by year Sort by 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, 2020	1096*	2020
Optimal hour ahead bidding in the real time electricity market DR Jiang, WB Powell INFORMS Journal on Computing 27 (3), 525-543, 2015	126	2015
An approximate dynamic programming algorithm for monotone value functions DR Jiang, WB Powell Operations Research 63 (6), 1489-1511, 2015	100	2015
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	70	2014
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	60	2020
Risk-averse approximate dynamic programming with quantile-based risk measures DR Jiang, WB Powell Mathematics of Operations Research 43 (2), 554-579, 2018	57*	2018
Dynamic inventory repositioning in on-demand rental networks S Benjaafar, D Jiang, X Li, X Li Management Science 68 (11), 7793-8514, 2022	51*	2022
Feedback-based tree search for reinforcement learning DR Jiang, E Ekwedike, H Liu International Conference on Machine Learning (ICML), 2284-2293, 2018	40	2018
Shape constraints in economics and operations research AL Johnson, DR Jiang Statistical Science 33 (4), 527-546, 2018	22	2018
Practicality of nested risk measures for dynamic electric vehicle charging DR Jiang, WB Powell https://arxiv.org/abs/1605.02848, 2017	20*	2017
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	19	2021
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
Lookahead-bounded Q-learning IE Shar, DR Jiang International Conference on Machine Learning (ICML), 2020	14*	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	11*	2021
Weakly coupled deep Q-networks IE Shar, DR Jiang Advances in Neural Information Processing Systems 37, 2023	6*	2023
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	6	2023
Pearl: A production-ready reinforcement learning agent Z Zhu, RS Braz, J Bhandari, D Jiang, Y Wan, Y Efroni, L Wang, R Xu, ... Journal of Machine Learning Research, 2024	4	2024
Aligned multi-objective optimization Y Efroni, D Jiang, B Kretzu, J Bhandari, Z Zhu, K Ullrich OPT 2024: Optimization for Machine Learning Workshop at NeurIPS, 2024	1	2024
On the linear speedup of personalized federated reinforcement learning with shared representations G Xiong, S Wang, D Jiang, J Li arXiv preprint arXiv:2411.15014, 2024	1*	2024
Dynamic subgoal-based exploration via Bayesian optimization Y Wang, M Poloczek, DR Jiang Transactions on Machine Learning Research, 2023	1*	2023

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