# Reduction Techniques for Survival Analysis

arXiv: https://arxiv.org/abs/2508.05715 · HTML: https://mlsanity.com/paper/2508.05715/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 120 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [Deep Recurrent Survival Analysis](https://mlsanity.com/paper/1809.02403/) (2018)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [Survival stacking: casting survival analysis as a classification problem](https://mlsanity.com/paper/2107.13480/) (2021)
- [A General Machine Learning Framework for Survival Analysis](https://mlsanity.com/paper/2006.15442/) (2020)
- [A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data](https://mlsanity.com/paper/2406.04098/) (2024)
- [Learning Reductions that Really Work](https://mlsanity.com/paper/1502.02704/) (2015)
- [Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression](https://mlsanity.com/paper/2004.07542/) (2020)
- [Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time](https://mlsanity.com/paper/2404.17211/) (2024)
- [Modeling the restricted mean survival time using pseudo-value random forests](https://mlsanity.com/paper/2411.01381/) (2024)

## Abstract (arXiv)

In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival analysis, as they allow for applying standard tools from machine and deep learning to many survival tasks without requiring custom learners. We provide an overview of different reduction techniques and discuss their respective strengths and weaknesses. We also provide a principled implementation of some of these reductions, such that they are directly available within standard machine learning workflows. We illustrate each reduction using dedicated examples and perform a benchmark analysis that compares their predictive performance to established machine learning methods for survival analysis.