Daniel R. Jiang

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 21

Top Topics

Approximate Dynamic Programming

Stochastic Approximation

Top Venues

Publications

Yijia Wang, Daniel R. Jiang
Faster Approximate Dynamic Programming by Freezing Slow States. CoRR (2023)
Yijia Wang, Matthias Poloczek, Daniel R. Jiang
Dynamic Subgoal-based Exploration via Bayesian Optimization. Trans. Mach. Learn. Res. 2023 (2023)
Ibrahim El Shar, Daniel R. Jiang
Weakly Coupled Deep Q-Networks. CoRR (2023)
Saif Benjaafar, Daniel R. Jiang, Xiang Li, Xiaobo Li
Dynamic Inventory Repositioning in On-Demand Rental Networks. Manag. Sci. 68 (11) (2022)
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. NeurIPS (2021)
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. CoRR (2021)
Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell
Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. Oper. Res. 68 (6) (2020)
Ibrahim El Shar, Daniel R. Jiang
Lookahead-Bounded Q-Learning. CoRR (2020)
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 (2020)
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)
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)
Ibrahim El Shar, Daniel R. Jiang
Lookahead-Bounded Q-learning. ICML (2020)
Yijia Wang, Matthias Poloczek, Daniel R. Jiang
Exploration via Sample-Efficient Subgoal Design. CoRR (2019)
Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR (2019)
Daniel R. Jiang, Emmanuel Ekwedike, Han Liu
Feedback-Based Tree Search for Reinforcement Learning. ICML (2018)
Daniel R. Jiang, Emmanuel Ekwedike, Han Liu
Feedback-Based Tree Search for Reinforcement Learning. CoRR (2018)
Daniel R. Jiang, Warren B. Powell
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures. Math. Oper. Res. 43 (2) (2018)
Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. CoRR (2017)
Daniel R. Jiang, Warren B. Powell
An Approximate Dynamic Programming Algorithm for Monotone Value Functions. Oper. Res. 63 (6) (2015)
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) (2015)
Daniel R. Jiang, Warren B. Powell
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures. CoRR (2015)
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)