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Adaptive Conformal Inference for Data Shifts by brickblack2009 is a document available to read on EtoBox.

This paper presents adaptive conformal inference (ACI) methods for creating prediction sets in online learning scenarios where data distributions may change over time. ACI builds on traditional conformal inference by continuously re-estimating a single parameter to ensure valid coverage of predictions, even under distribution shifts. The authors demonstrate the effectiveness of ACI through experiments on real-world datasets, showing its robustness against significant distribution changes.

Author

brickblack2009

Language

EN