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A Regret Perspective on Online Multiple Testing

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arXiv:2605. 13916v1 Announce Type: new Abstract: Online Multiple Testing (OMT), a fundamental pillar of sequential statistical inference, traditionally evaluates the False Discovery Rate (FDR) and statistical power in isolation, obscuring the highly asymmetric costs of false positives and false negatives in modern automated pipelines.

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Authors: Qingyang Hao, Kongchang Zhou, Fang Kong, Hongxin Wei
Released: 2026-05-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because A Regret Perspective on Online Multiple Testing overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, eval, line, rate, control, test. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.13916

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