3 citations · 5 across the 2 of their papers we have counts for
3 papers · 1 filter
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang, Daniel R. Jiang, Maximilian Balandat +3
Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a hig…
Lookahead-Bounded Q-Learning
Ibrahim El Shar, Daniel R. Jiang
We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably convergent variant of Q-learning that seeks to improve the performance of standard Q-learning in sto…
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel R. Jiang +4
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental…