  Survival regression aims to predict the time when an event of interest will
take place, typically a death or a failure. A fully parametric method [18] is
proposed to estimate the survival function as a mixture of individual
parametric distributions in the presence of censoring. In this paper, We
present a novel method to predict the survival time by better clustering the
survival data and combine primitive distributions. We propose two variants of
variational auto-encoder (VAE), discrete and continuous, to generate the latent
variables for clustering input covariates. The model is trained end to end by
jointly optimizing the VAE loss and regression loss. Thorough experiments on
dataset SUPPORT and FLCHAIN show that our method can effectively improve the
clustering result and reach competitive scores with previous methods. We
demonstrate the superior result of our model prediction in the long-term. Our
code is available at https://github.com/qinzzz/auton-survival-785.
