Survival Analysis (SA) is about modeling the time for an event of interest to occur, which has important applications in many fields, including medicine, defense, finance, and aerospace. Recent work has demonstrated the benefits of using Neural Networks (NNs) to capture complicated relationships in SA. However, the datasets used to train these models are often subject to uncertainty (e.g., noisy measurements, human error), which we show can substantially degrade the performance of existing techniques. To address this issue, this work leverages recent advances in NN verification to provide new algorithms for generating fully parametric survival models that are robust to such uncertainties. In particular, we introduce a robust loss function for training the models and use CROWN-IBP regularization to address the computational challenges with solving the resulting Min-Max problem. To evaluate the proposed approach, we apply relevant perturbations to publicly available datasets in the SurvSet repository and compare survival models against several baselines. We empirically show that Survival Analysis with Adversarial Regularization (SAWAR) method on average ranks best for dataset perturbations of varying magnitudes on metrics such as Negative Log Likelihood (NegLL), Integrated Brier Score (IBS), and Concordance Index (CI), concluding that adversarial regularization enhances performance in SA. Code: https://github.com/mlpotter/SAWAR

利用神经网络验证和对抗正则化的新算法，提出了一种稳健的参数生存模型，以解决生存分析中的数据不确定性问题，并通过实验证明对抗正则化可以提高性能。

鲁邦生存分析的对抗正则化