Reproduce “Learning Monotonic Probabilities with a Generative Cost Model” on OpenResearch

About this paper

Yongxiang Tang, Yanhua Cheng, Xiaocheng Liu, Chenchen Jiao, Yanxiang Zeng, Ning Luo, Pengjia Yuan, Xialong Liu and 1 more

Kuaishou researchers developed Generative Cost Models (GCM and IGCM) to learn monotonic probabilities by reformulating the problem into inferring a latent "cost" variable using variational inference. These models achieve superior accuracy in numerical quantile regression and competitive performance across various public datasets, with IGCM specifically addressing implicit monotonic relationships and demonstrating improved efficiency during inference.

Key insights

• The problem of learning strictly monotonic probabilities can be equivalently reformulated into identifying a partial order between an observable revenue variable and a latent, unobservable cost variable, simplifying the modeling task.
• Many real-world relationships exhibit implicit monotonicity, where variables are correlated through an underlying latent factor rather than strict causality, validating the need for models like IGCM.
• Generative models, particularly when combined with variational inference, can effectively enforce monotonic properties while maintaining high predictive accuracy and model flexibility, offering a powerful alternative to traditional construction or regularization methods.

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