标题： 随机原始对偶双块坐标双向部分 AUC 最大化 显示英文标题

标题： Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization

摘要： 双方向部分曲线下面积（TPAUC）是对不平衡数据二分类任务中的关键性能度量，因为它专注于真正例率（TPR）和假正例率（FPR）的特定范围。然而，针对TPAUC优化的随机算法研究仍显不足，现有方法要么局限于近似TPAUC损失函数，要么面临次优复杂度问题。为克服这些限制，我们提出了两种创新的随机主-对偶双块坐标算法用于TPAUC最大化。这些算法对主变量和对偶变量均采用随机块坐标更新，并适用于凸和非凸设置。我们提供了理论上的收敛速率分析，证明了相对于先前方法的重大改进。我们的实验结果基于多个基准数据集，验证了所提出算法的优越性能，展示了更快的收敛速度和更好的泛化能力。本研究推动了TPAUC优化领域的前沿发展，并为实际机器学习应用提供了实用工具。 显示英文摘要

摘要： Two-way partial AUC (TPAUC) is a critical performance metric for binary classification with imbalanced data, as it focuses on specific ranges of the true positive rate (TPR) and false positive rate (FPR). However, stochastic algorithms for TPAUC optimization remain under-explored, with existing methods either limited to approximated TPAUC loss functions or burdened by sub-optimal complexities. To overcome these limitations, we introduce two innovative stochastic primal-dual double block-coordinate algorithms for TPAUC maximization. These algorithms utilize stochastic block-coordinate updates for both the primal and dual variables, catering to both convex and non-convex settings. We provide theoretical convergence rate analyses, demonstrating significant improvements over prior approaches. Our experimental results, based on multiple benchmark datasets, validate the superior performance of our algorithms, showcasing faster convergence and better generalization. This work advances the state of the art in TPAUC optimization and offers practical tools for real-world machine learning applications.