Conference Papers Year : 2024

Dynamic Survival Analysis with Controlled Latent States

Abstract

We consider the task of learning individualspecific 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.
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Dates and versions

hal-04876033 , version 1 (09-01-2025)

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  • HAL Id : hal-04876033 , version 1

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Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian, Agathe Guilloux. Dynamic Survival Analysis with Controlled Latent States. ICML 2024 - Forty-First International Conference on Machine Learning, Jul 2024, Vienna, Austria. ⟨hal-04876033⟩
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