{"ID":2875024,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03297","arxiv_id":"2509.03297","title":"Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection","abstract":"We study online multiple testing with feedback, where decisions are made sequentially and the true state of the hypothesis is revealed after the decision has been made, either instantly or with a delay. We propose GAIF, a feedback-enhanced generalized alpha-investing framework that dynamically adjusts thresholds using revealed outcomes, ensuring finite-sample false discovery rate (FDR)/marginal FDR control. Extending GAIF to online conformal testing, we construct independent conformal $p$-values and introduce a feedback-driven model selection criterion to identify the best model/score, thereby improving statistical power. We demonstrate the effectiveness of our methods through numerical simulations and real-data applications.","short_abstract":"We study online multiple testing with feedback, where decisions are made sequentially and the true state of the hypothesis is revealed after the decision has been made, either instantly or with a delay. We propose GAIF, a feedback-enhanced generalized alpha-investing framework that dynamically adjusts thresholds using...","url_abs":"https://arxiv.org/abs/2509.03297","url_pdf":"https://arxiv.org/pdf/2509.03297v2","authors":"[\"Lin Lu\",\"Yuyang Huo\",\"Haojie Ren\",\"Zhaojun Wang\",\"Changliang Zou\"]","published":"2025-09-03T13:24:06Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"stat.ML\"]","methods":"[]","has_code":false}