Multicalibration Boosting: Theory, Convergence, and Transferability
Multicalibration extends classical calibration by requiring predictions to be unbiased over a rich collection of functions, encompassing both prediction slices and subpopulations. It has emerged as a powerful framework for fairness, robustness, and reliable prediction, yet the theoretical understanding of multicalibration boosting (MCBoost) remains fragmented and often relies on restrictive assumptions. In this work, we develop a unified and refined perspective on MCBoost that subsumes existing variants, including multiaccuracy, BatchGCP, and BatchMVP. We uncover several phenomena that provide new insights into its practical behavior: even highly accurate and flexible predictors can remai...
| Authors | Hanxuan Ye, Hongzhe Li |
|---|---|
| Primary category | stat.ML |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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