18 citations · 28 across the 14 of their papers we have counts for
10 papers · 1 filter
SMOTE-VAR: An Uncertainty-Aware Oversampling Method for Predicting Depression Remission in University Students
Dang Nguyen, Arun Kumar A, Taylor A. Braund +8
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well…
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…
Large Language Models for Imbalanced Classification: Diversity makes the difference
Dang Nguyen, Sunil Gupta, Kien Do +4
Oversampling is one of the most widely used approaches for addressing imbalanced classification. The core idea is to generate additional minority samples to rebalance the dataset.…
Generating Realistic Tabular Data with Large Language Models
Dang Nguyen, Sunil Gupta, Kien Do +2
While most generative models show achievements in image data generation, few are developed for tabular data generation. Recently, due to success of large language models (LLM) in d…
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…
Fast Conditional Network Compression Using Bayesian HyperNetworks
Phuoc Nguyen, Truyen Tran, Ky Le +6
We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal s…
Bayesian Optimization with Missing Inputs
Phuc Luong, Dang Nguyen, Sunil Gupta +2
Bayesian optimization (BO) is an efficient method for optimizing expensive black-box functions. In real-world applications, BO often faces a major problem of missing values in inpu…
DeepCoDA: personalized interpretability for compositional health data
Thomas P. Quinn, Dang Nguyen, Santu Rana +2
Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust. In the healthcare setting,…
Bayesian Optimization for Categorical and Category-Specific Continuous Inputs
Dang Nguyen, Sunil Gupta, Santu Rana +2
Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) method…
Control Matching via Discharge Code Sequences
Dang Nguyen, Wei Luo, Dinh Phung +1
In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220,000 patients. Our approach first leverages on Word2Vec framework to embed I…