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This first set of results resolves the main open question in Garg, Jung, Reingold, and Roth (SODA '24), showing that oracle-efficient online multicalibration with $\sqrt{T}$-type guarantees is possible in full generality. Furthermore, our GGM-style reduction unifies the analyses of existing online multicalibration algorithms, enables new algorithms for challenging environments with delayed observations or censored outcomes, and yields the first efficient black-box reduction between online learning and multiclass omniprediction.
Our second main result is a fine-grained reduction from high-dimensional online multicalibration to (contextual) $\Phi$-regret minimization. Together with our first result, this establishes a new route from external regret to Phi-regret that bypasses sophisticated fixed-point or semi-separation machinery, dramatically simplifies a result of Daskalakis, Farina, Fishelson, Pipis, and Schneider (STOC '25) while improving rates, and yields new algorithms that are robust to richer deviation classes, such as those belonging to any reproducing kernel Hilbert space.
| Subjects: | Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2604.19592 [cs.LG] |
| (or arXiv:2604.19592v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.19592 arXiv-issued DOI via DataCite (pending registration) |
From: Juan Carlos Perdomo [view email]
[v1]
Tue, 21 Apr 2026 15:43:22 UTC (36 KB)
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