Summary of Fastsurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models, by Jiachang Liu et al.
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
by Jiachang Liu, Rui Zhang, Cynthia Rudin
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this paper, researchers tackle the limitations of the widely used Cox proportional hazards (CPH) model in modern data science challenges. The CPH model is a crucial tool for survival analysis, with applications in healthcare, business, and manufacturing. However, current algorithms for training the CPH model struggle to converge due to vanishing second-order derivatives when dealing with high dimensionality and feature correlations. To overcome this issue, the authors propose new optimization methods that exploit hidden mathematical structures of the CPH model. These methods ensure monotonic loss decrease and global convergence, making them computationally efficient. The authors demonstrate the effectiveness of their approach by applying it to the cardinality-constrained CPH problem, producing sparse high-quality models that were previously impractical to construct. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Cox proportional hazards (CPH) model is an important tool for survival analysis, which has many applications in healthcare, business, and manufacturing. However, current algorithms for training the CPH model have some drawbacks when dealing with modern data science challenges such as high dimensionality and feature correlations. To solve this problem, researchers propose new optimization methods that can help the CPH model to converge better. These new methods are easy to use and ensure that the loss decreases in a monotonic way and converges globally. This means that they are computationally efficient and can be used to solve many problems. |
Keywords
* Artificial intelligence * Optimization
