mlsanityData to 14 Oct 2026

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

arXiv 2203.03094 ↗First posted 7 Mar 2022stat.ML cs.LGcited by 17

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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.

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

4 of 131 references.

References · 4

  1. 2014OpenML: networked science in machine learningcited by 526
  2. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
  3. 2017Machine Learning for Survival Analysis: A Surveycited by 112
  4. 2019The False Positive Control Lassocited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 7.

17 papers.

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  1. 2026Tabular Foundation Models Can Do Survival Analysiscited by 9
  2. 2023Heterogeneous Datasets for Federated Survival Analysis Simulationcited by 5
  3. 2024Optimal Survival Trees: A Dynamic Programming Approachcited by 5
  4. 2024A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Datacited by 5
  5. 2024FPBoost: Fully Parametric Gradient Boosting for Survival Analysiscited by 4
  6. 2026SurvPFN: Towards Foundation Models for Survival Predictionscited by 4
  7. 2026SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inferencecited by 3
  8. 2024FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Modelscited by 1
  9. 2025FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Financecited by 1
  10. 2026SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networkscited by 1
  11. 2026Functional Decomposition and Shapley Interactions for Interpreting Survival Modelscited by 1
  12. 2023Survival Analysis with Adversarial Regularizationcited by 0
5 more
  1. 2024SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recoverycited by 0
  2. 2025Neural Diffusion Processes for Physically Interpretable Survival Predictioncited by 0
  3. 2025Survival of the fittest Cox model: Pivotal variable selection for time-to-event datacited by 0
  4. 2026Discovering Subgroups with Exceptional Survival Characteristicscited by 0
  5. 2026Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Predictioncited by 0