Maximum Likelihood Estimation of Flexible Survival Densities with
  Importance Sampling
Mert Ketenci Shreyas Bhave Noémie Elhadad Adler Perotte
Survival analysis is a widely-used technique for analyzing time-to-event data
in the presence of censoring. In recent years, numerous survival analysis
methods have emerged which scale to large datasets and relax traditional
assumptions such as proportional hazards. These models, while being performant,
are very sensitive to model hyperparameters including: (1) number of bins and
bin size for discrete models and (2) number of cluster assignments for
mixture-based models. Each of these choices requires extensive tuning by
practitioners to achieve optimal performance. In addition, we demonstrate in
empirical studies that: (1) optimal bin size may drastically differ based on
the metric of interest (e.g., concordance vs brier score), and (2) mixture
models may suffer from mode collapse and numerical instability. We propose a
survival analysis approach which eliminates the need to tune hyperparameters
such as mixture assignments and bin sizes, reducing the burden on
practitioners. We show that the proposed approach matches or outperforms
baselines on several real-world datasets.