A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.
Machine learning: ECML 2002: 13th European conference on machine learning Helsinki, Finland, August 19--23, 2002 proceedings 13 , pages=
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A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.
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Instance-Adaptive Online Multicalibration
A single online multicalibration algorithm adaptively refines a dyadic grid and achieves instance-dependent rates: O(T^{2/3}) worst-case, O(sqrt T) for marginal stochastic data, and O(sqrt(JT)) for J-piecewise stationary means.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.