First approximate calibration results for discrete properties in multiclass settings via Lipschitz intermediaries for strongly orderable discrete properties.
arXiv preprint arXiv:2511.04907 , year=
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A single algorithm for online multicalibration achieves instance-adaptive rates by dynamically refining a dyadic prediction grid, recovering the worst-case Õ(T^{2/3}) bound and improving to Õ(√T) in marginal stochastic settings and Õ(√(JT)) for J-piecewise stationary means.
Multicalibration has minimax sample complexity Θ̃(ε^{-3}) when the number of groups is at most ε^{-κ} for fixed κ>0, versus Θ̃(ε^{-2}) for marginal calibration, with a sharp threshold at κ=0.
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Smoothed Elicitation Complexity for Approximate $\Gamma$-calibration of Discrete Classification Tasks
First approximate calibration results for discrete properties in multiclass settings via Lipschitz intermediaries for strongly orderable discrete properties.
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Instance-Adaptive Online Multicalibration
A single algorithm for online multicalibration achieves instance-adaptive rates by dynamically refining a dyadic prediction grid, recovering the worst-case Õ(T^{2/3}) bound and improving to Õ(√T) in marginal stochastic settings and Õ(√(JT)) for J-piecewise stationary means.
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The Sample Complexity of Multicalibration
Multicalibration has minimax sample complexity Θ̃(ε^{-3}) when the number of groups is at most ε^{-κ} for fixed κ>0, versus Θ̃(ε^{-2}) for marginal calibration, with a sharp threshold at κ=0.