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ORCIDDaniel R. Jiang
Person information
- affiliation: University of Pittsburgh, PA, USA
- affiliation (PhD 2016): Princeton University, NJ, USA
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Journal Articles
- 2023
[j6]Yijia Wang, Matthias Poloczek, Daniel R. Jiang:
Dynamic Subgoal-based Exploration via Bayesian Optimization. Trans. Mach. Learn. Res. 2023 (2023)- 2022
- [j5]Saif Benjaafar
, Daniel R. Jiang, Xiang Li, Xiaobo Li:
Dynamic Inventory Repositioning in On-Demand Rental Networks. Manag. Sci. 68(11): 7861-7878 (2022) - 2020
- [j4]Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell:
Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. Oper. Res. 68(6): 1678-1697 (2020) - 2018
- [j3]Daniel R. Jiang, Warren B. Powell:
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures. Math. Oper. Res. 43(2): 554-579 (2018) - 2015
- [j2]Daniel R. Jiang, Warren B. Powell:
Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming. INFORMS J. Comput. 27(3): 525-543 (2015) - [j1]Daniel R. Jiang, Warren B. Powell:
An Approximate Dynamic Programming Algorithm for Monotone Value Functions. Oper. Res. 63(6): 1489-1511 (2015)
Conference and Workshop Papers
- 2023
- [c9]Chu Wang, Yingfei Wang, Haipeng Luo, Daniel R. Jiang, Jinghai He, Zeyu Zheng:
2nd Workshop on Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond. KDD 2023: 5890 - 2022
- [c8]Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy:
Interpretable Personalized Experimentation. KDD 2022: 4173-4183 - 2021
- [c7]Daniel R. Jiang, Haipeng Luo, Chu Wang, Yingfei Wang:
Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond. KDD 2021: 4133-4134 - [c6]Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier:
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. NeurIPS 2021: 20197-20209 - 2020
- [c5]Ibrahim El Shar, Daniel R. Jiang:
Lookahead-Bounded Q-learning. ICML 2020: 8665-8675 - [c4]Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett:
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. NeurIPS 2020 - [c3]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS 2020 - 2018
- [c2]Daniel R. Jiang, Emmanuel Ekwedike, Han Liu:
Feedback-Based Tree Search for Reinforcement Learning. ICML 2018: 2289-2298 - 2014
- [c1]Daniel R. Jiang, Thuy V. Pham, Warren B. Powell, Daniel F. Salas, Warren R. Scott:
A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work? ADPRL 2014: 1-8
Informal and Other Publications
- 2024
- [i13]Jimmy Wang, Ethan Che, Daniel R. Jiang, Hongseok Namkoong:
AExGym: Benchmarks and Environments for Adaptive Experimentation. CoRR abs/2408.04531 (2024) - [i12]Ethan Che, Daniel R. Jiang, Hongseok Namkoong, Jimmy Wang:
Mathematical Programming For Adaptive Experiments. CoRR abs/2408.04570 (2024) - 2023
- [i11]Yijia Wang, Daniel R. Jiang:
Faster Approximate Dynamic Programming by Freezing Slow States. CoRR abs/2301.00922 (2023) - [i10]Ibrahim El Shar, Daniel R. Jiang:
Weakly Coupled Deep Q-Networks. CoRR abs/2310.18803 (2023) - 2021
- [i9]Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy:
Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. CoRR abs/2111.03267 (2021) - [i8]Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier:
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. CoRR abs/2111.06537 (2021) - 2020
- [i7]Ibrahim El Shar, Daniel R. Jiang:
Lookahead-Bounded Q-Learning. CoRR abs/2006.15690 (2020) - [i6]Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett:
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. CoRR abs/2006.15779 (2020) - 2019
- [i5]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR abs/1910.06403 (2019) - [i4]Yijia Wang, Matthias Poloczek, Daniel R. Jiang:
Exploration via Sample-Efficient Subgoal Design. CoRR abs/1910.09143 (2019) - 2018
- [i3]Daniel R. Jiang, Emmanuel Ekwedike, Han Liu:
Feedback-Based Tree Search for Reinforcement Learning. CoRR abs/1805.05935 (2018) - 2017
- [i2]Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell:
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. CoRR abs/1704.05963 (2017) - 2015
- [i1]Daniel R. Jiang, Warren B. Powell:
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures. CoRR abs/1509.01920 (2015)
Coauthor Index
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