Bradley Rava

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 high. Given a target accuracy, our goal is to minimize indecisions, observations we do not automate. For difficult problems, the target accuracy may be unattainable without abstention. By using indecisions, we can control the misclassification rate to any user-specified level, even below the Bayes optimal error rate, while minimizing overall indecision mass. We provide a complete characterization of the minimax risk in selective classification, establishing continuity and monotonicity properties that enable optimal indecision selection. We revisit selective inference via the Neyman-Pearson testing framework, where indecision enables control of type 2 error given fixed type 1 error probability. For both classification and testing, we propose a finite-sample calibration method with non-asymptotic guarantees, proving plug-in classifiers remain consistent and that accuracy-based calibration effectively controls indecision mass. In the binary Gaussian mixture model, we uncover the first sharp phase transition in selective inference, showing minimal indecision can yield near-optimal accuracy even under poor class separation. Experiments on Gaussian mixtures and real datasets confirm that small indecision proportions yield substantial accuracy gains, making indecision a principled tool for risk control.

Shunan Yao, Bradley Rava, Xin Tong et al.

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, important real-world applications, such as medical diagnosis and cybersecurity, have generated renewed interest in the Neyman-Pearson (NP) classification paradigm, which constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., the type II error). However, there has been little research on the NP paradigm under label noise. It is somewhat surprising that even when common NP classifiers ignore the label noise in the training stage, they are still able to control the type I error with high probability. However, the price they pay is excessive conservativeness of the type I error and hence a significant drop in power (i.e., $1 - $ type II error). Assuming that domain experts provide lower bounds on the corruption severity, we propose the first theory-backed algorithm that adapts most state-of-the-art classification methods to the training label noise under the NP paradigm. The resulting classifiers not only control the type I error with high probability under the desired level but also improve power.