A single algorithm for online multicalibration achieves instance-adaptive rates by dynamically refining a dyadic prediction grid, recovering the worst-case Õ(T^{2/3}) bound and improving to Õ(√T) in marginal stochastic settings and Õ(√(JT)) for J-piecewise stationary means.
arXiv preprint arXiv:2501.17205 , year=
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The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.
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Instance-Adaptive Online Multicalibration
A single algorithm for online multicalibration achieves instance-adaptive rates by dynamically refining a dyadic prediction grid, recovering the worst-case Õ(T^{2/3}) bound and improving to Õ(√T) in marginal stochastic settings and Õ(√(JT)) for J-piecewise stationary means.
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Theoretical Foundations of Conformal Prediction
The book curates and presents proofs of important existing results in conformal prediction in a unified pedagogical format with illustrations.