  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.
