Survival Analysis provides critical insights for partially incomplete
time-to-event data in various domains. It is also an important example of
probabilistic machine learning. The probabilistic nature of the predictions can
be exploited by using (proper) scoring rules in the model fitting process
instead of likelihood-based optimization. Our proposal does so in a generic
manner and can be used for a variety of model classes. We establish different
parametric and non-parametric sub-frameworks that allow different degrees of
flexibility. Incorporated into neural networks, it leads to a computationally
efficient and scalable optimization routine, yielding state-of-the-art
predictive performance. Finally, we show that using our framework, we can
recover various parametric models and demonstrate that optimization works
equally well when compared to likelihood-based methods.

生存分析提供了关于各个领域中部分不完整的事件发生时间数据的关键洞察。它也是概率机器学习的一个重要示例。我们的提案以一种通用的方式利用了预测的概率特性，使用（适当的）评分规则而不是基于似然的优化来进行模型拟合过程。我们建立了不同的参数和非参数子框架，允许不同程度的灵活性。结合神经网络，它导致了一种计算高效和可扩展的优化程序，产生了最先进的预测性能。最后，我们展示了使用我们的框架，我们能够恢复各种参数模型，并证明在与基于似然方法相比时，优化同样有效。