{"ID":2882678,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09592","arxiv_id":"2508.09592","title":"Online Prediction with Limited Selectivity","abstract":"Selective prediction [Dru13, QV19] models the scenario where a forecaster freely decides on the prediction window that their forecast spans. Many data statistics can be predicted to a non-trivial error rate without any distributional assumptions or expert advice, yet these results rely on that the forecaster may predict at any time. We introduce a model of Prediction with Limited Selectivity (PLS) where the forecaster can start the prediction only on a subset of the time horizon. We study the optimal prediction error both on an instance-by-instance basis and via an average-case analysis. We introduce a complexity measure that gives instance-dependent bounds on the optimal error. For a randomly-generated PLS instance, these bounds match with high probability.","short_abstract":"Selective prediction [Dru13, QV19] models the scenario where a forecaster freely decides on the prediction window that their forecast spans. Many data statistics can be predicted to a non-trivial error rate without any distributional assumptions or expert advice, yet these results rely on that the forecaster may predic...","url_abs":"https://arxiv.org/abs/2508.09592","url_pdf":"https://arxiv.org/pdf/2508.09592v1","authors":"[\"Licheng Liu\",\"Mingda Qiao\"]","published":"2025-08-13T08:17:12Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DS\"]","methods":"[]","has_code":false}