3 papers
cs.LG2024
Preventing Conflicting Gradients in Neural Marked Temporal Point Processes
Tanguy Bosser, Souhaib Ben Taieb
Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictiv…
cs.LG2024
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
Victor Dheur, Tanguy Bosser, Rafael Izbicki +1
Sequences of labeled events observed at irregular intervals in continuous time are ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical framewor…
cs.LG2023
On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data
Tanguy Bosser, Souhaib Ben Taieb
Temporal Point Processes (TPPs) serve as the standard mathematical framework for modeling asynchronous event sequences in continuous time. However, classical TPP models are often c…