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  "path": "/abs/2602.17577v1",
  "publishedAt": "2026-02-20T01:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Lunjia Hu",
    "Kevin Tian",
    "Chutong Yang"
  ],
  "textContent": "**Authors:** Lunjia Hu, Kevin Tian, Chutong Yang\n\nOmniprediction 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.",
  "title": "Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction"
}