82 citations · 149 across the 15 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
stat.ME2021★ 1 cited
Non-splitting Neyman-Pearson Classifiers
Jingming Wang, Lucy Xia, Zhigang Bao +1
The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., t…
stat.ML2021
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…