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  <title><![CDATA[Continuously Generalized Ordinal Regression for Linear and Deep Models]]></title>
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  <description><![CDATA[Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach for modeling ordinal data involves fitting parallel separating hyperplanes that optimize a certain loss function. This assumption offers sample efficient learning via inductive bias, but is often too restrictive in real-world datasets where features may have varying effects across different categories. Allowing cla...]]></description>
  <dc:date>2022-04-28</dc:date>
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