# Spectral Survival Analysis

arXiv: https://arxiv.org/abs/2505.22641 · HTML: https://mlsanity.com/paper/2505.22641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 89 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [A Scalable Discrete-Time Survival Model for Neural Networks](https://mlsanity.com/paper/1805.00917/) (2018)
- [Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework](https://mlsanity.com/paper/1801.05512/) (2018)
- [Survival Regression with Proper Scoring Rules and Monotonic Neural Networks](https://mlsanity.com/paper/2103.14755/) (2021)
- [SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks](https://mlsanity.com/paper/2008.08637/) (2020)
- [Neural Likelihoods via Cumulative Distribution Functions](https://mlsanity.com/paper/1811.00974/) (2018)
- [Image-based Survival Analysis for Lung Cancer Patients using CNNs](https://mlsanity.com/paper/1808.09679/) (2018)
- [Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions](https://mlsanity.com/paper/2303.10358/) (2023)
- [Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models](https://mlsanity.com/paper/1309.7936/) (2013)
- [FastCPH: Efficient Survival Analysis for Neural Networks](https://mlsanity.com/paper/2208.09793/) (2022)
- [DATELINE: Deep Plackett-Luce Model with Uncertainty Measurements](https://mlsanity.com/paper/1812.05877/) (2018)
- [Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression](https://mlsanity.com/paper/2307.08044/) (2023)

## Abstract (arXiv)

Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. We identify a fundamental connection between rank regression and the CoxPH model: this allows us to adapt and extend the so-called spectral method for rank regression to survival analysis. Our approach is versatile, naturally generalizing to several CoxPH variants, including deep models. We empirically verify our method's scalability on multiple real-world high-dimensional datasets; our method outperforms legacy methods w.r.t. predictive performance and efficiency.