The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification

ArXi:2510.01020v2 Announce Type: replace We study sequential testing for a binary disease outcome when risk follows an unknown logistic model. At each round, the decision maker may either pay for a test revealing the true label or predict the outcome based on patient features and past data. The goal is to minimize costly tests while ensuring the misclassification rate stays below $\alpha$ with probability at least $1-\delta