3 papers
cs.LG2025
Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning
Sishun Liu, Ke Deng, Xiuzhen Zhang +1
Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustwort…
cs.LG2023
Explainable History Distillation by Marked Temporal Point Process
Sishun Liu, Ke Deng, Yan Wang +1
Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this pape…
cs.LG2023
Intensity-free Integral-based Learning of Marked Temporal Point Processes
Sishun Liu, Ke Deng, Xiuzhen Zhang +1
In the marked temporal point processes (MTPP), a core problem is to parameterize the conditional joint PDF (probability distribution function) for inter-event time a…