arXiv Machine Learning
Explicit Asymptotic Bounds for Sequential Calibration Beyond $T^{2/3}$
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arXiv Machine Learning
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Breaking the $T^{2/3}$ Barrier for Sequential Calibration
arXiv:2406. 13668v4 Announce Type: replace Abstract: A set of probabilistic forecasts is calibrated if each prediction of the forecaster closely approximates the empirical distribution of outcomes on the subset of timesteps where that prediction was made.
arXiv Machine Learning
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Efficient Sequential Calibration with $O(T^{2/3-\epsilon})$ Error Bound
arXiv:2607. 12928v1 Announce Type: new Abstract: We study the online binary sequential calibration problem.
arXiv Machine Learning
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Optimal Recalibration of an Online Predictor
arXiv:2607. 19689v1 Announce Type: cross Abstract: We study the problem of recalibrating an online predictor [KE17, OKS24]: given an arbitrary "hint" sequence of forecasts, the learner must output new predictions that are calibrated while incurring small excess error relative to the original forecasts, under a proper loss.
arXiv Machine Learning
Jul 10
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
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Truthful Calibration Measures for Sequential Prediction
The paper investigates the feasibility of exact truthfulness in calibration measures for sequential binary prediction. It proves that exact truthfulness cannot coexist with completeness and soundness, even when outcomes are independent. The authors then provide two reductions that transform any base calibration measure into additively or multiplicatively approximately truthful ones, achieving a multiplicative truthfulness guarantee that improves upon previous results.
arXiv Machine Learning
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Proper Calibeating
arXiv:2605. 26703v2 Announce Type: replace-cross Abstract: The classic concept of "calibrated forecasts" and its more recent refinement, "calibeating," are defined with respect to the standard quadratic scoring rule.