202 citations · 242 across the 14 of their papers we have counts for
14 papers
Faster Reinforcement Learning by Freezing Slow States
Yijia Wang, Daniel R. Jiang
We study infinite horizon Markov decision processes (MDPs) with "fast-slow" structure, where some state variables evolve rapidly ("fast states") while others change more gradually…
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat +2
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimi…
Interpretable Personalized Experimentation
Han Wu, Sarah Tan, Weiwei Li +7
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments. However, they…
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…
Dynamic Subgoal-based Exploration via Bayesian Optimization
Yijia Wang, Matthias Poloczek, Daniel R. Jiang
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration. Motivated by comp…