mlsanityData to 7 Oct 2026

Dynamical Survival Analysis with Controlled Latent States

arXiv 2401.17077 ↗First posted 30 Jan 2024stat.ML cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 33 of its 75 references that are in the map. First posted on arXiv 30 Jan 2024.

Builds on

33 of 75 references.

References · 33

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2021Convex optimizationcited by 2,479
  3. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  4. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 501
  5. 2018Lipschitz regularity of deep neural networks: analysis and efficient estimationcited by 413
  6. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
  7. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
  8. 2019Neural Jump Stochastic Differential Equationscited by 147
  9. 2019Fully Neural Network based Model for General Temporal Point Processescited by 119
  10. 2021Neural Temporal Point Processes: A Reviewcited by 113
  11. 2020Neural Spatio-Temporal Point Processescited by 92
  12. 2019Deep Signature Transformscited by 75
21 more
  1. 2021Neural Controlled Differential Equations for Online Prediction Taskscited by 46
  2. 2020Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPUcited by 45
  3. 2018The iisignature library: efficient calculation of iterated-integral signatures and log signaturescited by 38
  4. 2022Signature Methods in Machine Learningcited by 33
  5. 2020Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Reviewcited by 32
  6. 2019Embedding and learning with signaturescited by 29
  7. 2021Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processescited by 27
  8. 2020SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networkscited by 26
  9. 2023Neural signature kernels as infinite-width-depth-limits of controlled ResNetscited by 25
  10. 2021Framing RNN as a kernel method: A neural ODE approachcited by 23
  11. 2022Predicting the impact of treatments over time with uncertainty aware neural differential equationscited by 23
  12. 2022Scaling ResNets in the Large-depth Regimecited by 18
  13. 2023Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Timecited by 14
  14. 2022SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) predictioncited by 13
  15. 2019The Brier Score under Administrative Censoring: Problems and Solutionscited by 12
  16. 2020Functional linear regression with truncated signaturescited by 6
  17. 2023Learning the Dynamics of Sparsely Observed Interacting Systemscited by 3
  18. 2017Controlled Singular Volterra Integral Equations and Pontryagin Maximum Principlecited by 1
  19. 2018Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome frameworkcited by 1
  20. 2022Fast and flexible inference for joint models of multivariate longitudinal and survival data using Integrated Nested Laplace Approximationscited by 1
  21. 2023An efficient joint model for high dimensional longitudinal and survival data via generic association featurescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 1.

7 papers.

  1. 2025Position: Stop Chasing the C-index when Evaluating Survival Analysis Modelscited by 5
  2. 2025TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Predictioncited by 4
  3. 2024SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal datacited by 1
  4. 2025SigBERT: Combining Narrative Medical Reports and Rough Path Signature Theory for Survival Risk Estimation in Oncologycited by 0
  5. 2025KANFormer for Predicting Fill Probabilities via Survival Analysis in Limit Order Bookscited by 0
  6. 2025Subgroup Discovery with the Cox Modelcited by 0
  7. 2026MultiSigBERT: Beyond Survival Analysis through Multimodal and Sequential Modeling in Oncologycited by 0