Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Raul Astudillo, Daniel Jiang, Max Balandat, Eytan Bakshy, Peter I. Frazier
I am a Research Scientist at Meta on the Core Data Science team, where I work on a variety of problems related to sequential decision making under uncertainty. My research interests are in the areas of reinforcement learning, approximate dynamic programming, and Bayesian optimization. I received my PhD in Operations Research & Financial Engineering from Princeton University in 2016 and am also affiliated with the University of Pittsburgh as an assistant professor (currently on leave) in Industrial Engineering.
Interests
Reinforcement learning, approximate dynamic programming, Bayesian optimization, operations research
Latest Publications
NeurIPS - 2021
Learning Efficient Interpretable Policies on Experimental Data
Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Hanson Wang, Daniel Jiang, Adam Obeng, Eytan Bakshy
Lookahead-Bounded Q-Learning
Ibrahim El Shar, Daniel Jiang
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang, Daniel Jiang, Max Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization
Max Balandat, Brian Karrer, Daniel Jiang, Sam Daulton, Ben Letham, Andrew Gordon Wilson, Eytan Bakshy