Sample-efficient Multiclass Calibration under $\ell_{p}$ Error
No confident code implementation found for this paper. 1 possible match can be shown below.
Abstract
Calibrating a multiclass predictor, that outputs a distribution over labels, is particularly challenging due to the exponential number of possible prediction values. In this work, we propose a new definition of calibration error that interpolates between two established calibration error notions, one with known exponential sample complexity and one with polynomial sample complexity for calibrating a given predictor. Our algorithm can calibrate any given predictor for the entire range of interpolation, except for one endpoint, using only a polynomial number of samples. At the other endpoint, we achieve nearly optimal dependence on the error parameter, improving upon previous work. A key technical contribution is a novel application of adaptive data analysis with high adaptivity but only logarithmic overhead in the sample complexity.
