Binary classification with corrupted labels

Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification

We study the problem of fair binary classification using the notion of Equal Opportunity. It requires the true positive rate to distribute equally across the sensitive groups. Within this setting we show that the fair optimal classifier is obtained by recalibrating the Bayes classifier by a group-dependent threshold. We provide a constructive expression for the threshold. This result motivates us to devise a plug-in classification procedure based on both unlabeled and labeled datasets. While the latter is used to learn the output conditional probability, the former is used for calibration. The…

基于简单二元决策的分布式多类分类的基本限制

Fundamental limits of distributed multiclass classification from simple binary decisions

研究从\(O(\log K)\)个简单二元分类器组合构建\(K\)类分类器的问题，当二元分类器为超平面时，在高斯设置下得出性能界限，并通过模拟实验验证理论结果。

High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization

Label Shift has been widely believed to be harmful to the generalization performance of machine learning models. Researchers have proposed many approaches to mitigate the impact of the label shift, e.g., balancing the training data. However, these methods often consider the underparametrized regime, where the sample size is much larger than the data dimension. The research under the overparametrized regime is very limited. To bridge this gap, we propose a new asymptotic analysis of the Fisher Linear Discriminant classifier for binary classification with label shift. Specifically, we prove that…