Dynamic Survival Analysis with Controlled Latent States

par Linus Bleistein

vendredi 25 oct. 2024, 10:30 → 11:30 Europe/Paris

Salle Séminaire 1, au sous-sol (La Doua)

URL pour suivre à distance : https://univ-lyon1.webex.com/univ-lyon1/j.php?MTID=m9f9f31be272350a858d6b1e9dc37f2e2

We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel modelization approach in which the intensity is the solution to a controlled differential equation. We first design a neural estimator by building on neural controlled differential equations. In a second time, we show that our model can be linearized in the signature space under sufficient regularity conditions, yielding a signature-based estimator which we call CoxSig. We provide theoretical learning guarantees for both estimators, before showcasing the performance of our models on a vast array of simulated and real-world datasets from finance, predictive maintenance and food supply chain management. This is joint work with Van-Tuan Nguyen, Adeline Fermanian and Agathe Guilloux.