82 citations · 149 across the 15 of their papers we have counts for
4 papers · 1 filter
Asymmetric error control under imperfect supervision: a label-noise-adjusted Neyman-Pearson umbrella algorithm
Shunan Yao, Bradley Rava, Xin Tong +1
Label noise in data has long been an important problem in supervised learning applications as it affects the effectiveness of many widely used classification methods. Recently, imp…
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
Wei Vivian Li, Xin Tong, Jingyi Jessica Li
Asymmetric binary classification problems, in which the type I and II errors have unequal severity, are ubiquitous in real-world applications. To handle such asymmetry, researchers…
Neyman-Pearson Classification under High-Dimensional Settings
Anqi Zhao, Yang Feng, Lie Wang +1
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosi…
Neyman-Pearson classification, convexity and stochastic constraints
Philippe Rigollet, Xin Tong
Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a fi…