3 citations · 6 across the 4 of their papers we have counts for
5 papers
Neyman-Pearson Classification with Indecisions: Simultaneous Control of Selective Type I and Type II Errors
Bradley Rava, Bowen Gang
Binary classification often requires controlling both type I and type II error rates at prespecified levels. When the class distributions overlap substantially, the two error limit…
Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing
Mohamed Ndaoud, Peter Radchenko, Bradley Rava
Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is hi…
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…
A Burden Shared is a Burden Halved: A Fairness-Adjusted Approach to Classification
Bradley Rava, Wenguang Sun, Gareth M. James +1
We investigate the fairness issue in classification, where automated decisions are made for individuals from different protected groups. In high-consequence scenarios, decision err…
Irrational Exuberance: Correcting Bias in Probability Estimates
Gareth M. James, Peter Radchenko, Bradley Rava
We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased es…