<ul><li>In machine learning tasks, maintaining monotonic relationships between input and output variables is crucial.</li><li>Traditional methods rely on construction or regularization techniques to achieve monotonicity.</li><li>A new approach, the Generative Cost Model (GCM), addresses strict monotonic probability by modeling a latent cost variable.</li><li>The Implicit Generative Cost Model (IGCM) is proposed to handle implicit monotonic relationships, outperforming existing techniques in experiments.</li></ul>

Learning Monotonic Probabilities with a Generative Cost Model

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