• The Korean Journal of Applied Statistics
• /
• v.37 no.6
• /
• pp.733-749
• /
• 2024

In survival analysis, the use of deep neural networks has become popular. It requires the mini-batch type stochastic gradient descent (SGD) algorithm. However, the existence of risk set in the partial likelihood can be problematic, which can be addressed by many previous works. In this paper, we proposed an advanced algorithm compared to the conventional SGD by applying an online mirror descent algorithm. It can be used for any convex optimization problem where the given tasks are closely related to online learning. A re-parameterization trick and bi-level optimization are used to construct the algorithm. The experiments on various setups reveal the superiority of the proposed algorithm. It can contribute to making an efficient mini-batch-based algorithm over the convex optimization and semi-parametric survival models.