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[2504.15206] How Global Calibration Strengthens Multiaccuracy
Summary
This article explores how global calibration enhances multiaccuracy in machine learning, revealing its potential to improve predictive fairness and learning outcomes.
Why It Matters
Understanding the interplay between multiaccuracy and global calibration is crucial for developing fair and effective machine learning models. This research provides insights that can influence future algorithms and applications in various fields, particularly in ensuring equitable predictions across diverse groups.
Key Takeaways
- Multiaccuracy alone is limited in its predictive power.
- Global calibration significantly enhances the effectiveness of multiaccuracy.
- Calibrated multiaccuracy can achieve optimal density in predictions.
- The study highlights the complementary roles of multiaccuracy and calibration.
- Insights from this research can guide future developments in fair machine learning.
Computer Science > Machine Learning arXiv:2504.15206 (cs) [Submitted on 21 Apr 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:How Global Calibration Strengthens Multiaccuracy Authors:Sílvia Casacuberta, Parikshit Gopalan, Varun Kanade, Omer Reingold View a PDF of the paper titled How Global Calibration Strengthens Multiaccuracy, by S\'ilvia Casacuberta and 3 other authors View PDF HTML (experimental) Abstract:Multiaccuracy and multicalibration are multigroup fairness notions for prediction that have found numerous applications in learning and computational complexity. They can be achieved from a single learning primitive: weak agnostic learning. Here we investigate the power of multiaccuracy as a learning primitive, both with and without the additional assumption of calibration. We find that multiaccuracy in itself is rather weak, but that the addition of global calibration (this notion is called calibrated multiaccuracy) boosts its power substantially, enough to recover implications that were previously known only assuming the stronger notion of multicalibration. We give evidence that multiaccuracy might not be as powerful as standard weak agnostic learning, by showing that there is no way to post-process a multiaccurate predictor to get a weak learner, even assuming the best hypothesis has correlation $1/2$. Rather, we show that it yields a restricted form of weak agnostic learning, which requires some concept in the class to have correlation greater than ...
