# SurvSet: An open-source time-to-event dataset repository

arXiv: https://arxiv.org/abs/2203.03094 · HTML: https://mlsanity.com/paper/2203.03094/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 131 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Machine Learning for Survival Analysis: A Survey](https://mlsanity.com/paper/1708.04649/) (2017)
- [The False Positive Control Lasso](https://mlsanity.com/paper/1903.12584/) (2019)

## Built on by (most cited first, 17 of 17)

- [Tabular Foundation Models Can Do Survival Analysis](https://mlsanity.com/paper/2601.22259/) (2026)
- [Heterogeneous Datasets for Federated Survival Analysis Simulation](https://mlsanity.com/paper/2301.12166/) (2023)
- [Optimal Survival Trees: A Dynamic Programming Approach](https://mlsanity.com/paper/2401.04489/) (2024)
- [A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data](https://mlsanity.com/paper/2406.04098/) (2024)
- [FPBoost: Fully Parametric Gradient Boosting for Survival Analysis](https://mlsanity.com/paper/2409.13363/) (2024)
- [SurvPFN: Towards Foundation Models for Survival Predictions](https://mlsanity.com/paper/2606.04564/) (2026)
- [SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference](https://mlsanity.com/paper/2605.15488/) (2026)
- [FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models](https://mlsanity.com/paper/2410.19081/) (2024)
- [FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance](https://mlsanity.com/paper/2507.14160/) (2025)
- [SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2602.02179/) (2026)
- [Functional Decomposition and Shapley Interactions for Interpreting Survival Models](https://mlsanity.com/paper/2602.16505/) (2026)
- [Survival Analysis with Adversarial Regularization](https://mlsanity.com/paper/2312.16019/) (2023)
- [SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery](https://mlsanity.com/paper/2402.19016/) (2024)
- [Neural Diffusion Processes for Physically Interpretable Survival Prediction](https://mlsanity.com/paper/2510.00733/) (2025)
- [Survival of the fittest Cox model: Pivotal variable selection for time-to-event data](https://mlsanity.com/paper/2510.19374/) (2025)
- [Discovering Subgroups with Exceptional Survival Characteristics](https://mlsanity.com/paper/2602.22179/) (2026)
- [Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Prediction](https://mlsanity.com/paper/2609.04901/) (2026)

## Abstract (arXiv)

Time-to-event (T2E) analysis is a branch of statistics that models the duration of time it takes for an event to occur. Such events can include outcomes like death, unemployment, or product failure. Most modern machine learning (ML) algorithms, like decision trees and kernel methods, are supported for T2E modelling with data science software (python and R). To complement these developments, SurvSet is the first open-source T2E dataset repository designed for a rapid benchmarking of ML algorithms and statistical methods. The data in SurvSet have been consistently formatted so that a single preprocessing method will work for all datasets. SurvSet currently has 76 datasets which vary in dimensionality, time dependency, and background (the majority of which come from biomedicine). SurvSet is available on PyPI and can be installed with pip install SurvSet. R users can download the data directly from the corresponding git repository.