# Variational Learning of Individual Survival Distributions

arXiv: https://arxiv.org/abs/2003.04430 · HTML: https://mlsanity.com/paper/2003.04430/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction](https://mlsanity.com/paper/2204.09633/) (2022)
- [The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models](https://mlsanity.com/paper/2203.00144/) (2022)
- [Censor Dependent Variational Inference](https://mlsanity.com/paper/2502.09591/) (2025)
- [Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out](https://mlsanity.com/paper/2303.10255/) (2023)
- [Variational Deep Survival Machines: Survival Regression with Censored Outcomes](https://mlsanity.com/paper/2404.15595/) (2024)

## Abstract (arXiv)

The abundance of modern health data provides many opportunities for the use of machine learning techniques to build better statistical models to improve clinical decision making. Predicting time-to-event distributions, also known as survival analysis, plays a key role in many clinical applications. We introduce a variational time-to-event prediction model, named Variational Survival Inference (VSI), which builds upon recent advances in distribution learning techniques and deep neural networks. VSI addresses the challenges of non-parametric distribution estimation by (i) relaxing the restrictive modeling assumptions made in classical models, and (ii) efficiently handling the censored observations, it i.e., events that occur outside the observation window, all within the variational framework. To validate the effectiveness of our approach, an extensive set of experiments on both synthetic and real-world datasets is carried out, showing improved performance relative to competing solutions.