Summary of Dynamical Survival Analysis with Controlled Latent States, by Linus Bleistein et al.
Dynamical Survival Analysis with Controlled Latent States
by Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian, Agathe Guilloux
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to learn individual-specific intensities of counting processes using static variables and irregularly sampled time series. A neural estimator is developed by building on neural controlled differential equations, which can be linearized in the signature space under sufficient regularity conditions, yielding a signature-based estimator called CoxSig. Theoretical learning guarantees are provided for both estimators, and their performance is showcased on a variety of simulated and real-world datasets from finance, predictive maintenance, and food supply chain management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to measure the intensity of events happening at different rates in people or things. It uses a special type of math problem called a controlled differential equation. The researchers created two ways to solve this problem: one using neural networks and another that simplifies it by looking at patterns in the data. They tested these methods on lots of fake and real data from areas like finance, maintenance, and food supply chain management. |
Keywords
* Artificial intelligence * Time series
