Dynamical Survival Analysis with Controlled Latent States

Autor:	Bleistein, Linus, Nguyen, Van-Tuan, Fermanian, Adeline, Guilloux, Agathe
Rok vydání:	2024
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ICML 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.17077 Zobrazit plný text záznamu View this record from Arxiv