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

翻译：生存分析（Survival Analysis, SA）旨在对感兴趣事件发生的时间进行建模，在医学、国防、金融和航空航天等多个领域具有重要应用。近期研究表明，使用神经网络（Neural Networks, NNs）捕捉SA中的复杂关系具有显著优势。然而，训练这些模型所用的数据集常存在不确定性（例如噪声测量、人为错误），我们证明这种不确定性会显著降低现有技术的性能。为解决该问题，本研究利用神经网络验证领域的最新进展，提出了生成对这类不确定性具有鲁棒性的全参数化生存模型的新算法。具体而言，我们引入了一种鲁棒损失函数用于模型训练，并采用CROWN-IBP正则化方法解决由此产生的极小极大优化问题中的计算挑战。为评估所提方法，我们对SurvSet数据库中的公开数据集施加相关扰动，并将生存模型与多个基线方法进行比较。实验结果表明，基于对抗正则化的生存分析方法（Survival Analysis with Adversarial Regularization, SAWAR）在不同幅度的数据集扰动下，其负对数似然（NegLL）、综合布里尔分数（IBS）和一致性指数（CI）等指标的平均排名最优，从而证实对抗正则化能有效提升SA性能。代码：https://github.com/mlpotter/SAWAR