A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work? - Daniel R. Jiang; Thuy V. Pham; Warren B. Powell; Daniel F. Salas; Warren R. Scott (ADPRL, 2014)

Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming. - Daniel R. Jiang; Warren B. Powell (INFORMS J. Comput., 2015)

An Approximate Dynamic Programming Algorithm for Monotone Value Functions. - Daniel R. Jiang; Warren B. Powell (Oper. Res., 2015)

Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. - Daniel R. Jiang; Lina Al-Kanj; Warren B. Powell (CoRR, 2017)

Feedback-Based Tree Search for Reinforcement Learning. - Daniel R. Jiang; Emmanuel Ekwedike; Han Liu 001 (ICML, 2018)

Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures. - Daniel R. Jiang; Warren B. Powell (Math. Oper. Res., 2018)

BoTorch: Programmable Bayesian Optimization in PyTorch. - Maximilian Balandat; Brian Karrer; Daniel R. Jiang; Samuel Daulton; Benjamin Letham; Andrew Gordon Wilson; Eytan Bakshy (CoRR, 2019)

Exploration via Sample-Efficient Subgoal Design. - Yijia Wang; Matthias Poloczek; Daniel R. Jiang (CoRR, 2019)

Lookahead-Bounded Q-learning. - Ibrahim El Shar; Daniel R. Jiang (ICML, 2020)

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. - Shali Jiang 001; Daniel R. Jiang; Maximilian Balandat; Brian Karrer; Jacob R. Gardner; Roman Garnett (NeurIPS, 2020)

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. - Maximilian Balandat; Brian Karrer; Daniel R. Jiang; Samuel Daulton; Benjamin Letham; Andrew Gordon Wilson; Eytan Bakshy (NeurIPS, 2020)

Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. - Daniel R. Jiang; Lina Al-Kanj; Warren B. Powell (Oper. Res., 2020)

Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond. - Daniel R. Jiang; Haipeng Luo; Chu Wang; Yingfei Wang (KDD, 2021)

Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. - Raul Astudillo; Daniel R. Jiang; Maximilian Balandat; Eytan Bakshy; Peter I. Frazier (NeurIPS, 2021)

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. - Han Wu; Sarah Tan; Weiwei Li 006; Mia Garrard; Adam Obeng; Drew Dimmery; Shaun Singh; Hanson Wang; Daniel R. Jiang; Eytan Bakshy (CoRR, 2021)

Interpretable Personalized Experimentation. - Han Wu; Sarah Tan; Weiwei Li 006; Mia Garrard; Adam Obeng; Drew Dimmery; Shaun Singh; Hanson Wang; Daniel R. Jiang; Eytan Bakshy (KDD, 2022)

Dynamic Inventory Repositioning in On-Demand Rental Networks. - Saif Benjaafar; Daniel R. Jiang; Xiang Li; Xiaobo Li 002 (Manag. Sci., 2022)

2nd Workshop on Multi-Armed Bandits and Reinforcement Learning: Advancing Decision Making in E-Commerce and Beyond. - Chu Wang; Yingfei Wang; Haipeng Luo; Daniel R. Jiang; Jinghai He; Zeyu Zheng (KDD, 2023)

Faster Approximate Dynamic Programming by Freezing Slow States. - Yijia Wang; Daniel R. Jiang (CoRR, 2023)

Weakly Coupled Deep Q-Networks. - Ibrahim El Shar; Daniel R. Jiang (CoRR, 2023)

Dynamic Subgoal-based Exploration via Bayesian Optimization. - Yijia Wang; Matthias Poloczek; Daniel R. Jiang (Trans. Mach. Learn. Res., 2023)

AExGym: Benchmarks and Environments for Adaptive Experimentation. - Jimmy Wang; Ethan Che; Daniel R. Jiang; Hongseok Namkoong (CoRR, 2024)

Mathematical Programming For Adaptive Experiments. - Ethan Che; Daniel R. Jiang; Hongseok Namkoong; Jimmy Wang (CoRR, 2024)

Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank. - Wenhao Zhan; Scott Fujimoto; Zheqing Zhu; Jason D. Lee; Daniel R. Jiang; Yonathan Efroni (CoRR, 2024)

Improving Generative Ad Text on Facebook using Reinforcement Learning. - Daniel R. Jiang; Alex Nikulkov; Yu-Chia Chen; Yang Bai; Zheqing Zhu (CoRR, 2025)