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arXiv · 2602.17577

Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction

Lunjia Hu·Kevin Tian·Chutong Yang
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Abstract

Omniprediction is a learning problem that requires suboptimality bounds for each of a family of losses $\mathcal{L}$ against a family of comparator predictors $\mathcal{C}$. We initiate the study of omniprediction in a multiclass setting, where the comparator family $\mathcal{C}$ may be infinite. Our main result is an extension of the recent binary omniprediction algorithm of [OKK25] to the multiclass setting, with sample complexity (in statistical settings) or regret horizon (in online settings) $\approx \varepsilon^{-(k+1)}$, for $\varepsilon$-omniprediction in a $k$-class prediction problem. En route to proving this result, we design a framework of potential broader interest for solving Blackwell approachability problems where multiple sets must simultaneously be approached via coupled actions.

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Lunjia Hu, Kevin Tian, Chutong Yang. 2026-02-19. Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction. https://arxiv.org/abs/2602.17577

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