14 citations · 14 across the 6 of their papers we have counts for
7 papers
A Statistical Approach to Estimating Sample Size of Machine Learning Models
Dat Phan-Trong, Sunil Gupta, Svetha Venkatesh
Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and e…
Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method
Dang Nguyen, Bao Duong, Arun Kumar +12
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well…
High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions
Giang Ngo, Dat Phan Trong, Dang Nguyen +1
Level set estimation (LSE) classifies whether an unknown function's value exceeds a specified threshold for given inputs, a fundamental problem in many real-world applications. In…
Adaptive Acquisition Selection for Bayesian Optimization with Large Language Models
Giang Ngo, Dat Phan Trong, Dang Nguyen +2
Bayesian Optimization critically depends on the choice of acquisition function, but no single strategy is universally optimal; the best choice is non-stationary and problem-depende…
Active Level Set Estimation for Continuous Search Space with Theoretical Guarantee
Giang Ngo, Dang Nguyen, Dat Phan-Trong +1
A common problem encountered in many real-world applications is level set estimation where the goal is to determine the region in the function domain where the function is above or…
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…
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…