  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.
