June 11, 2024, 4:45 a.m. | Elise Han, Chengpiao Huang, Kaizheng Wang

stat.ML updates on arXiv.org arxiv.org

arXiv:2406.06516v1 Announce Type: cross
Abstract: Distribution-free prediction sets play a pivotal role in uncertainty quantification for complex statistical models. Their validity hinges on reliable calibration data, which may not be readily available as real-world environments often undergo unknown changes over time. In this paper, we propose a strategy for choosing an adaptive window and use the data therein to construct prediction sets. The window is selected by optimizing an estimated bias-variance tradeoff. We provide sharp coverage guarantees for our method, …

abstract arxiv calibration cs.lg data distribution drift environments free inference paper pivotal prediction predictive quantification role statistical stat.me stat.ml strategy temporal type uncertainty world

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