25 citations · 50 across the 14 of their papers we have counts for
4 papers · 1 filter
High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
Quoc-Anh Hoang Nguyen, The Hung Tran
Bayesian Optimization (BO) is a popular approach to optimizing expensive-to-evaluate black-box functions. Despite the success of BO, its performance may decrease exponentially as t…
Bayesian Optimization for Unknown Cost-Varying Variable Subsets with No-Regret Costs
Vu Viet Hoang, Quoc Anh Hoang Nguyen, Hung Tran The
Bayesian Optimization (BO) is a widely-used method for optimizing expensive-to-evaluate black-box functions. Traditional BO assumes that the learner has full control over all query…
Policy Learning for Off-Dynamics RL with Deficient Support
Linh Le Pham Van, Hung The Tran, Sunil Gupta
Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In m…
PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks
Dat Phan-Trong, Hung The Tran, Alistair Shilton +1
Black-box optimization is a powerful approach for discovering global optima in noisy and expensive black-box functions, a problem widely encountered in real-world scenarios. Recent…