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.
arXiv preprint arXiv:2309.12236 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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Expert calibration suffices for MoE calibration under distribution shifts in hard-routed models but not soft-routed ones; adversarial reweighting improves the accuracy-calibration tradeoff across models and shifts.
Develops pseudo-labels from ordered p-value spacings to enable post-hoc calibration assessment of local FDR estimates in multiple testing without labels, revealing miscalibration of q-values in empirical literature.
citing papers explorer
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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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Toward Calibrated Mixture-of-Experts Under Distribution Shift
Expert calibration suffices for MoE calibration under distribution shifts in hard-routed models but not soft-routed ones; adversarial reweighting improves the accuracy-calibration tradeoff across models and shifts.
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Calibration without labels in multiple testing
Develops pseudo-labels from ordered p-value spacings to enable post-hoc calibration assessment of local FDR estimates in multiple testing without labels, revealing miscalibration of q-values in empirical literature.