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[2602.17577] Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction
Summary
This paper explores the concept of omniprediction in a multiclass setting, extending existing algorithms to address suboptimality bounds against infinite comparator families, with implications for machine learning and decision-making frameworks.
Why It Matters
Understanding omniprediction in multiclass scenarios is crucial for advancing machine learning algorithms, particularly in applications requiring robust decision-making under uncertainty. This research contributes to the theoretical foundation and practical applications of learning algorithms, enhancing their effectiveness in complex environments.
Key Takeaways
- Introduces a multiclass extension of omniprediction algorithms.
- Addresses suboptimality bounds in complex prediction scenarios.
- Proposes a framework for solving simultaneous Blackwell approachability problems.
- Highlights the significance of sample complexity and regret horizon in learning.
- Offers insights into the implications for future machine learning applications.
Computer Science > Data Structures and Algorithms arXiv:2602.17577 (cs) [Submitted on 19 Feb 2026] Title:Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction Authors:Lunjia Hu, Kevin Tian, Chutong Yang View a PDF of the paper titled Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction, by Lunjia Hu and 2 other authors View PDF HTML (experimental) 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. Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2602.17577 [cs.DS] (or arXiv:2602.17577v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2602.17577 Focus to learn more arXiv-issued D...
