Conformance Checking Using Activity and Trace Embeddings. - Jari Peeperkorn; Seppe vanden Broucke; Jochen De Weerdt (BPM (Forum), 2020)

Supervised Conformance Checking Using Recurrent Neural Network Classifiers. - Jari Peeperkorn; Seppe vanden Broucke; Jochen De Weerdt (ICPM Workshops, 2020)

Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis. - Jari Peeperkorn; Seppe vanden Broucke; Jochen De Weerdt (ICPM Workshops, 2021)

Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring. - Alexander Stevens; Johannes De Smedt; Jari Peeperkorn (ICPM Workshops, 2021)

Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors. - Jarne Vandenabeele; Gilles Vermaut; Jari Peeperkorn; Jochen De Weerdt (ATAED@Petri Nets, 2022)

Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs. - Jari Peeperkorn; Carlos Ortega Vázquez; Alexander Stevens; Johannes De Smedt; Seppe vanden Broucke; Jochen De Weerdt (ICPM Workshops, 2022)

Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks. - Alexander Stevens; Johannes De Smedt; Jari Peeperkorn; Jochen De Weerdt (ICPM, 2022)

Discovering high-quality process models despite data scarcity. - Jan Niklas Adams; Jari Peeperkorn; Tobias Brockhoff; Isabelle Terrier; Heiko Göhner; Merih Seran Uysal; Seppe vanden Broucke; Jochen De Weerdt; Wil M. P. van der Aalst (ER (Companion), 2023)

Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration. - Thais Rodrigues Neubauer; Jari Peeperkorn; Sarajane Marques Peres; Jochen De Weerdt; Marcelo Fantinato (ICMLA, 2023)

Manifold Learning for Adversarial Robustness in Predictive Process Monitoring. - Alexander Stevens; Jari Peeperkorn; Johannes De Smedt; Jochen De Weerdt (ICPM, 2023)

Global conformance checking measures using shallow representation and deep learning. - Jari Peeperkorn; Seppe vanden Broucke; Jochen De Weerdt (Eng. Appl. Artif. Intell., 2023)

Can recurrent neural networks learn process model structure? - Jari Peeperkorn; Seppe vanden Broucke; Jochen De Weerdt (J. Intell. Inf. Syst., 2023)

Multivariate Approaches for Process Model Forecasting. - Yongbo Yu; Jari Peeperkorn; Johannes De Smedt; Jochen De Weerdt (ICPM Workshops, 2024)

Dynamic and Scalable Data Preparation for Object-Centric Process Mining. - Lien Bosmans; Jari Peeperkorn; Alexandre Goossens; Giovanni Lugaresi; Johannes De Smedt; Jochen De Weerdt (CoRR, 2024)

Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) - Core Model, Design Space, and Lessons Learned. - Dirk Fahland; Marco Montali; Julian Lebherz; Wil M. P. van der Aalst; Maarten van Asseldonk; Peter Blank; Lien Bosmans; Marcus Brenscheidt; Claudio Di Ciccio; Andrea Delgado 001; Daniel Calegari; Jari Peeperkorn; et al. (CoRR, 2024)

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders. - Alexander Stevens; Jari Peeperkorn; Johannes De Smedt; Jochen De Weerdt (CoRR, 2024)

Validation set sampling strategies for predictive process monitoring. - Jari Peeperkorn; Seppe vanden Broucke; Jochen De Weerdt (Inf. Syst., 2024)

Achieving Group Fairness Through Independence in Predictive Process Monitoring. - Jari Peeperkorn; Simon De Vos (CAiSE (1), 2025)

Model-driven Stochastic Trace Clustering. - Jari Peeperkorn; Johannes De Smedt; Jochen De Weerdt (CoRR, 2025)