5 papers
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
Zhipeng He, Alexander Stevens, Chun Ouyang +3
Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations m…
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular Data
Zhipeng He, Chun Ouyang, Lijie Wen +2
Adversarial attacks pose a significant threat to machine learning models by inducing incorrect predictions through imperceptible perturbations to input data. While these attacks ar…
Curation and Analysis of MIMICEL -- An Event Log for MIMIC-IV Emergency Department
Jia Wei, Chun Ouyang, Bemali Wickramanayake +3
The global issue of overcrowding in emergency departments (ED) necessitates the analysis of patient flow through ED to enhance efficiency and alleviate overcrowding. However, tradi…
DALL-M: Context-Aware Clinical Data Augmentation with LLMs
Chihcheng Hsieh, Catarina Moreira, Isabel Blanco Nobre +5
X-ray images are vital in medical diagnostics, but their effectiveness is limited without clinical context. Radiologists often find chest X-rays insufficient for diagnosing underly…
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
Zhipeng He, Chun Ouyang, Laith Alzubaidi +2
Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks ha…