The minimax rate of estimating second-order calibration error is Õ(1/√n) with a matching Ω(1/√n) lower bound, enabled by analyticity from the sech kernel and yielding the first finite-sample guarantee for second-order Platt scaling.
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , pages=
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Conditional risk calibration reduces to standard regression and is distinct from probability calibration.
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
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The Minimax Rate of Second-Order Calibration
The minimax rate of estimating second-order calibration error is Õ(1/√n) with a matching Ω(1/√n) lower bound, enabled by analyticity from the sech kernel and yielding the first finite-sample guarantee for second-order Platt scaling.
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Calibrating conditional risk
Conditional risk calibration reduces to standard regression and is distinct from probability calibration.
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Quantifying and Predicting Disagreement in Graded Human Ratings
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.