6 papers · 1 filter
Variance Driven Exploration: A Provable and Efficient Methodology for Pure Exploration in Highly Stochastic Environments
Khang Luong, Nam Nguyen, Hoang Ta +2
We propose Variance Driven Exploration (VarDE), a principled approach for pure exploration in highly stochastic environments, where the exploration process is dominated by stochast…
Using Synthetic Data to estimate the True Error is theoretically and practically doable
Hai Hoang Thanh, Duy-Tung Nguyen, Hung The Tran +1
Accurately evaluating model performance is crucial for deploying machine learning systems in real-world applications. Traditional methods often require a sufficiently large labeled…
DmC: Nearest Neighbor Guidance Diffusion Model for Offline Cross-domain Reinforcement Learning
Linh Le Pham Van, Minh Hoang Nguyen, Duc Kieu +3
Cross-domain offline reinforcement learning (RL) seeks to enhance sample efficiency in offline RL by utilizing additional offline source datasets. A key challenge is to identify an…
High Dimensional Bayesian Optimization using Lasso Variable Selection
Vu Viet Hoang, Hung The Tran, Sunil Gupta +1
Bayesian optimization (BO) is a leading method for optimizing expensive black-box optimization and has been successfully applied across various scenarios. However, BO suffers from…
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…