  We describe a new approach to estimating relative risks in time-to-event
prediction problems with censored data in a fully parametric manner. Our
approach does not require making strong assumptions of constant proportional
hazard of the underlying survival distribution, as required by the
Cox-proportional hazard model. By jointly learning deep nonlinear
representations of the input covariates, we demonstrate the benefits of our
approach when used to estimate survival risks through extensive experimentation
on multiple real world datasets with different levels of censoring. We further
demonstrate advantages of our model in the competing risks scenario. To the
best of our knowledge, this is the first work involving fully parametric
estimation of survival times with competing risks in the presence of censoring.
