Summary of Online Learning Approach For Survival Analysis, by Camila Fernandez (lpsm) et al.
Online Learning Approach for Survival Analysis
by Camila Fernandez, Pierre Gaillard, Joseph de Vilmarest, Olivier Wintenberger
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed online mathematical framework enables real-time adaptation to dynamic environments and censored data in survival analysis. The framework relies on an optimal second-order online convex optimization algorithm, Online Newton Step (ONS), which provides explicit algorithms with non-asymptotic convergence guarantees. The ONS hyperparameters can be selected using a stochastic approach that ensures logarithmic stochastic regret. An adaptive aggregation method is also introduced to ensure robustness in hyperparameter selection while maintaining fast regret bounds. The framework’s findings can extend beyond survival analysis and are relevant for any case characterized by poor exp-concavity and unstable ONS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new online mathematical framework that helps analyze survival data in real-time, even when the data is incomplete or delayed. This framework uses an algorithm called Online Newton Step (ONS) to estimate the time it takes for an event to happen. The ONS algorithm is special because it has guarantees that it will converge quickly and accurately. The researchers also show how to choose the right settings for the ONS algorithm, which is important for getting good results. |
Keywords
* Artificial intelligence * Hyperparameter * Optimization
