activity
20212026
most citedBuilding Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms

47 citations · 54 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Deep Time-Series Models Meet Volatility: Multi-Horizon Electricity Price Forecasting in the Australian National Electricity Market

Mohammed Osman Gani, Zhipeng He, Chun Ouyang +1

Accurate electricity price forecasting (EPF) is increasingly difficult in markets characterised by extreme volatility, frequent price spikes, and rapid structural shifts. Deep lear…

cs.LG2025

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…

cs.DB2025

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…

cs.LG2025★ 2 cited

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…

cs.LG2024★ 5 cited

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…

cs.LG2021★ 47 cited

Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms

Bemali Wickramanayake, Zhipeng He, Chun Ouyang +3

In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Pre…