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An Elementary Predictor Obtaining $2\sqrt{T}+1$ Distance to Calibration

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arxiv 2402.11410 v2 pith:B47ZNCP6 submitted 2024-02-18 cs.LG cs.DSstat.ML

classification cs.LGcs.DSstat.ML
keywords calibrationdistanceerrorsqrtalgorithmefficientpredictorproblem
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abstract

Blasiok et al. [2023] proposed distance to calibration as a natural measure of calibration error that unlike expected calibration error (ECE) is continuous. Recently, Qiao and Zheng [2024] gave a non-constructive argument establishing the existence of an online predictor that can obtain $O(\sqrt{T})$ distance to calibration in the adversarial setting, which is known to be impossible for ECE. They leave as an open problem finding an explicit, efficient algorithm. We resolve this problem and give an extremely simple, efficient, deterministic algorithm that obtains distance to calibration error at most $2\sqrt{T}+1$.

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  1. Tractable Agreement Protocols

    cs.LG 2024-11 conditional novelty 8.0 of 10

    Conversation-calibrated agents, efficiently constructible from any ML model, reach approximate agreement in few rounds while improving accuracy, generalizing Aumann-Aaronson theorems to d dimensions and action feedback.

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