Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective-猫眼课题宝

DOI：

10.48550/arxiv.2401.14343

发表时间：

2024-01

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Xuechen Zhang;Mingchen Li;Jiasi Chen;Christos Thrampoulidis;Samet Oymak

现代分类问题在各个类别之间表现出异质性：每个类别可能具有独特的属性，例如样本大小，标签质量或可预测性（容易与困难），以及测试时的变量重要性。如果不加注意，这些异质性会阻碍学习过程，最明显的是，在优化公平目标时。在高斯混合设置下，我们证明了平衡精度的最佳SVM分类器需要适应类属性。这促使我们提出CAP：一种有效的和通用的方法，产生一个类特定的学习策略（例如，hyperparameters），基于该类的属性。这样，优化过程更好地适应异质性。CAP导致了对为每个类分配单独超参数的天真方法的实质性改进。我们实例CAP的损失函数设计和事后logit调整，强调标签不平衡的问题。我们表明，CAP与现有技术相比具有竞争力，其灵活性为平衡准确性之外的公平性目标带来了明显的好处。最后，我们评估CAP的标签噪声问题，以及加权测试目标，以展示如何CAP可以共同适应不同的异质性。

Modern classification problems exhibit heterogeneities across individual classes: Each class may have unique attributes, such as sample size, label quality, or predictability (easy vs difficult), and variable importance at test-time. Without care, these heterogeneities impede the learning process, most notably, when optimizing fairness objectives. Confirming this, under a gaussian mixture setting, we show that the optimal SVM classifier for balanced accuracy needs to be adaptive to the class attributes. This motivates us to propose CAP: An effective and general method that generates a class-specific learning strategy (e.g.~hyperparameter) based on the attributes of that class. This way, optimization process better adapts to heterogeneities. CAP leads to substantial improvements over the naive approach of assigning separate hyperparameters to each class. We instantiate CAP for loss function design and post-hoc logit adjustment, with emphasis on label-imbalanced problems. We show that CAP is competitive with prior art and its flexibility unlocks clear benefits for fairness objectives beyond balanced accuracy. Finally, we evaluate CAP on problems with label noise as well as weighted test objectives to showcase how CAP can jointly adapt to different heterogeneities.

Class-attribute Priors: Adapting Optimization to Heterogeneity and Fairness Objective

文献摘要

相似文献