10 citations · 10 across the 4 of their papers we have counts for
5 papers · 1 filter
Pareto Set Learning for Multi-Objective Reinforcement Learning
Erlong Liu, Yu-Chang Wu, Xiaobin Huang +4
Multi-objective decision-making problems have emerged in numerous real-world scenarios, such as video games, navigation and robotics. Considering the clear advantages of Reinforcem…
Monte Carlo Tree Search based Space Transfer for Black-box Optimization
Shukuan Wang, Ke Xue, Lei Song +2
Bayesian optimization (BO) is a popular method for computationally expensive black-box optimization. However, traditional BO methods need to solve new problems from scratch, leadin…
Offline Multi-Objective Optimization
Ke Xue, Rong-Xi Tan, Xiaobin Huang +1
Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. In addition to the objective function being black-box and expe…
Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation
Xiaobin Huang, Lei Song, Ke Xue +1
Bayesian optimization (BO) is a sample-efficient method and has been widely used for optimizing expensive black-box functions. Recently, there has been a considerable interest in B…
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
Lei Song, Ke Xue, Xiaobin Huang +1
Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of…