25 citations · 50 across the 14 of their papers we have counts for
15 papers
Hybrid Cross-domain Robust Reinforcement Learning
Linh Le Pham Van, Minh Hoang Nguyen, Hung Le +2
Robust reinforcement learning (RL) aims to learn policies that remain effective despite uncertainties in its environment, which frequently arise in real-world applications due to v…
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…
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
Huong Ha, Vu Nguyen, Hung Tran-The +3
Gaussian process (GP) based Bayesian optimization (BO) is a powerful method for optimizing black-box functions efficiently. The practical performance and theoretical guarantees of…
Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
Dat Phan-Trong, Hung Tran-The, Sunil Gupta
Bayesian Optimization (BO) is an effective approach for global optimization of black-box functions when function evaluations are expensive. Most prior works use Gaussian processes…
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Hien Dang, Tho Tran, Stanley Osher +3
Modern deep neural networks have achieved impressive performance on tasks from image classification to natural language processing. Surprisingly, these complex systems with massive…
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
Hung Tran-The, Sunil Gupta, Santu Rana +1
The expected improvement (EI) algorithm is one of the most popular strategies for optimization under uncertainty due to its simplicity and efficiency. Despite its popularity, the t…
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support
Hung Tran-The, Sunil Gupta, Thanh Nguyen-Tang +2
We address policy learning with logged data in contextual bandits. Current offline-policy learning algorithms are mostly based on inverse propensity score (IPS) weighting requiring…
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana +1
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bou…
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks
Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The +1
Offline reinforcement learning (RL) leverages previously collected data for policy optimization without any further active exploration. Despite the recent interest in this problem,…
Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces
Hung Tran-The, Sunil Gupta, Santu Rana +2
Bayesian optimisation is a popular method for efficient optimisation of expensive black-box functions. Traditionally, BO assumes that the search space is known. However, in many pr…
Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization
Hung Tran-The, Sunil Gupta, Santu Rana +1
Scaling Bayesian optimisation (BO) to high-dimensional search spaces is a active and open research problems particularly when no assumptions are made on function structure. The mai…
Bayesian Optimization with Unknown Search Space
Huong Ha, Santu Rana, Sunil Gupta +3
Applying Bayesian optimization in problems wherein the search space is unknown is challenging. To address this problem, we propose a systematic volume expansion strategy for the Ba…