Two new papers published on arXiv explore the theoretical underpinnings of multicalibration in machine learning. The first paper establishes tight lower bounds for online multicalibration, demonstrating an information-theoretic separation from marginal calibration. The second paper investigates the sample complexity of multicalibration in the batch setting, proving that $\widetilde{\Theta}(\varepsilon^{-3})$ samples are necessary and sufficient for achieving a certain error tolerance. AI
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IMPACT These theoretical findings may inform the development of more robust and fair machine learning models by clarifying the fundamental limits of calibration.
RANK_REASON The cluster contains two academic papers published on arXiv concerning theoretical aspects of machine learning calibration.
