Augmenting post-hoc explanations for predictive process monitoring with uncertainty quantification via conformalized Monte Carlo dropoutNijat Mehdiyev; Maxim Majlatow; Peter FettkeIn: Data & Knowledge Engineering (DKE), Vol. 156 (March 2025), Pages 1-29, Elsevier B.V. 12/2024.
Counterfactual Explanations in the Big Picture: An Approach for Process Prediction-Driven Job-Shop Scheduling OptimizationNijat Mehdiyev; Maxim Majlatow; Peter FettkeIn: Cognitive Computation, Vol. 16, No. 5, Pages 2674-2700, Springer, 9/2024.
Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspectiveNijat Mehdiyev; Maxim Majlatow; Peter FettkeIn: Annals of Operations Research (AOR), Pages 1-40, Springer, 4/2024.
