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• W2167788644 abstract "Many problems involve the prediction of multiple, possibly dependent labels. The structured output prediction framework builds predictors that take these dependencies into account and use them to improve accuracy. In many such tasks, performance is evaluated by the Area Under the ROC Curve (AUC). While a framework for optimizing the AUC loss for unstructured models exists, it does not naturally extend to structured models. In this work, we propose a representation and learning formulation for optimizing structured models over the AUC loss, show how our approach generalizes the unstructured case, and provide algorithms for solving the resulting inference and learning problems. We also explore several new variants of the AUC measure which naturally arise from our formulation. Finally, we empirically show the utility of our approach in several domains." @default.
• W2167788644 created "2016-06-24" @default.
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• W2167788644 date "2014-04-02" @default.
• W2167788644 modified "2023-09-23" @default.
• W2167788644 title "Learning Structured Models with the AUC Loss and Its Generalizations" @default.
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• W2167788644 hasPublicationYear "2014" @default.
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