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arXiv Machine Learning By Prabhav Singh, Xiheng Tom Wang, Haojun Shi, Jason Eisner

Marformer: A Transformer for Predicting Missing Data Distributions

Read the original on arXiv Machine Learning →

The Marformer is a Transformer model designed to predict conditional marginal distributions of missing variables given observed data, enabling computation of Bayes risk and Value of Information. It learns a hidden-vector representation for each variable’s distribution and refines these through attention, similar to BERT’s masked language modeling but for probabilistic inference. Evaluations on synthetic Bayesian networks, discretized Gaussians, structured annotation data, and a real annotation dataset show that the Marformer matches or surpasses classical missing-data methods while being faster and not requiring explicit joint distribution modeling.

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