Marginalization Model

Marginalization models aim to efficiently handle missing data or integrate out nuisance variables in complex probabilistic models, improving prediction accuracy and scalability. Current research focuses on developing efficient approximation techniques for marginalization within various model architectures, including autoregressive models, generative models, and knowledge graph embeddings, often employing Monte Carlo methods or novel training strategies like "Knockout." This work is significant because it addresses a critical limitation in many machine learning applications, enabling more robust and scalable inference in diverse fields ranging from time series analysis to generative modeling of high-dimensional data.