5 citations · 8 across the 6 of their papers we have counts for
6 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…
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…
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…
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data
Mythreyi Velmurugan, Chun Ouyang, Yue Xu +3
Explainable Artificial Intelligence (XAI) techniques are used to provide transparency to complex, opaque predictive models. However, these techniques are often designed for image a…
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…
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
Alexander Stevens, Chun Ouyang, Johannes De Smedt +1
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics. Nevertheless, the inherent opacity…