{"ID":2862158,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.01020","arxiv_id":"2510.01020","title":"The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification","abstract":"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 $α$ with probability at least $1-δ$. We propose a method that jointly estimates the logistic parameter $θ^{\\star}$ and the feature distribution, using a conservative threshold on the logistic score to decide when to test. We prove our procedure achieves the target error with high probability and requires only $\\widetilde O(\\sqrt{T})$ more tests than an oracle with full knowledge. This is the first no-regret guarantee for error-constrained logistic testing, with direct applications to medical screening. Simulations corroborate our theoretical results, showing safe classification of patients and efficient estimation of $θ^{\\star}$ with few excess tests.","short_abstract":"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 misclassifica...","url_abs":"https://arxiv.org/abs/2510.01020","url_pdf":"https://arxiv.org/pdf/2510.01020v2","authors":"[\"Tavor Z. Baharav\",\"Spyros Dragazis\",\"Aldo Pacchiano\"]","published":"2025-10-01T15:28:00Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"math.ST\",\"stat.ML\"]","methods":"[]","has_code":false}