论文标题
基于等级的多任务学习公平回归
Rank-Based Multi-task Learning for Fair Regression
论文作者
论文摘要
在这项工作中，我们使用基于偏见的培训数据集的多任务回归模型开发了一种新颖的公平学习方法，该模型使用流行的基于秩的非参数独立性测试，即Mann Whitney U统计量，以测量目标变量和受保护变量之间的依赖关系。为了有效地解决这一学习问题，我们首先将问题重新制定为一个新的非凸优化问题，在该问题中，基于单个对象的小组排名函数来定义非convex约束。然后，我们基于非凸线交替方向方法（NC-ADMM）的框架开发有效的模型训练算法，其中主要挑战之一是对基于排名功能定义的前面的非convex集实施有效的投影甲骨文。通过对合成和现实世界数据集的广泛实验，我们验证了我们的新方法的表现，以针对几种与公平学习相关的几种流行指标进行了几种最先进的竞争方法。
In this work, we develop a novel fairness learning approach for multi-task regression models based on a biased training dataset, using a popular rank-based non-parametric independence test, i.e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables. To solve this learning problem efficiently, we first reformulate the problem as a new non-convex optimization problem, in which a non-convex constraint is defined based on group-wise ranking functions of individual objects. We then develop an efficient model-training algorithm based on the framework of non-convex alternating direction method of multipliers (NC-ADMM), in which one of the main challenges is to implement an efficient projection oracle to the preceding non-convex set defined based on ranking functions. Through the extensive experiments on both synthetic and real-world datasets, we validated the out-performance of our new approach against several state-of-the-art competitive methods on several popular metrics relevant to fairness learning.
