# Adversarial Time-to-Event Modeling

arXiv: https://arxiv.org/abs/1804.03184 · HTML: https://mlsanity.com/paper/1804.03184/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2018.

## Built on by (most cited first, 30 of 33)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [Survival Regression with Proper Scoring Rules and Monotonic Neural Networks](https://mlsanity.com/paper/2103.14755/) (2021)
- [SurvTRACE: Transformers for Survival Analysis with Competing Events](https://mlsanity.com/paper/2110.00855/) (2021)
- [A Deep Variational Approach to Clustering Survival Data](https://mlsanity.com/paper/2106.05763/) (2021)
- [SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2110.14001/) (2021)
- [AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images](https://mlsanity.com/paper/2212.06515/) (2022)
- [auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data](https://mlsanity.com/paper/2204.07276/) (2022)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes](https://mlsanity.com/paper/2410.01086/) (2024)
- [Counterfactual Phenotyping with Censored Time-to-Events](https://mlsanity.com/paper/2202.11089/) (2022)
- [Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee](https://mlsanity.com/paper/2206.10477/) (2022)
- [Survival Mixture Density Networks](https://mlsanity.com/paper/2208.10759/) (2022)
- [The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models](https://mlsanity.com/paper/2203.00144/) (2022)
- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
- [Explainable Predictive Maintenance](https://mlsanity.com/paper/2306.05120/) (2023)
- [MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Risk](https://mlsanity.com/paper/2202.00882/) (2022)
- [A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information](https://mlsanity.com/paper/2305.06862/) (2023)
- [Generating Accurate Synthetic Survival Data by Conditioning on Outcomes](https://mlsanity.com/paper/2405.17333/) (2024)
- [Deep State-Space Generative Model For Correlated Time-to-Event Predictions](https://mlsanity.com/paper/2407.19371/) (2024)
- [Augmenting High-dimensional Nonlinear Optimization with Conditional GANs](https://mlsanity.com/paper/2103.04748/) (2021)
- [Deep Neural Network Based Accelerated Failure Time Models using Rank Loss](https://mlsanity.com/paper/2206.05974/) (2022)
- [Modelling customer churn for the retail industry in a deep learning based sequential framework](https://mlsanity.com/paper/2304.00575/) (2023)
- [Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression](https://mlsanity.com/paper/2307.08044/) (2023)
- [NSOTree: Neural Survival Oblique Tree](https://mlsanity.com/paper/2309.13825/) (2023)
- [Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning](https://mlsanity.com/paper/2406.14009/) (2024)
- [Learning Survival Distributions with the Asymmetric Laplace Distribution](https://mlsanity.com/paper/2505.03712/) (2025)
- [Metaparametric Neural Networks for Survival Analysis](https://mlsanity.com/paper/2110.06610/) (2021)
- [Hazard Gradient Penalty for Survival Analysis](https://mlsanity.com/paper/2205.13717/) (2022)
- [Survival Analysis with Adversarial Regularization](https://mlsanity.com/paper/2312.16019/) (2023)

## Abstract (arXiv)

Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statistical models. We present a deep-network-based approach that leverages adversarial learning to address a key challenge in modern time-to-event modeling: nonparametric estimation of event-time distributions. We also introduce a principled cost function to exploit information from censored events (events that occur subsequent to the observation window). Unlike most time-to-event models, we focus on the estimation of time-to-event distributions, rather than time ordering. We validate our model on both benchmark and real datasets, demonstrating that the proposed formulation yields significant performance gains relative to a parametric alternative, which we also propose.