202 citations · 242 across the 14 of their papers we have counts for
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math.OC2019
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…
cs.LG2019★ 202 cited
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…