Generative Distribution Prediction: A Unified Approach to Multimodal Learning

Abstract

Abstract Accurate prediction for multimodal data—including tabular, textual, and visual inputs or outputs—is essential for advancing analytics across diverse application domains. Existing methods often struggle to integrate heterogeneous data types while maintaining strong predictive performance. We introduce Generative Distribution Prediction (GDP), a model-agnostic framework that leverages high-fidelity multimodal synthetic data generated from the conditional distribution of interest, such as via conditional diffusion models, to enhance prediction across both structured and unstructured modalities. GDP is compatible with any expressive generative model and naturally supports transfer learning for domain adaptation. We provide a rigorous theoretical foundation for GDP, establishing statistical guarantees on its predictive accuracy when diffusion models serve as the generative backbone. By estimating the underlying data-generating distribution and enabling loss-adapted risk minimization, GDP delivers accurate point predictions in broad multimodal settings. We empirically validate GDP on a range of supervised learning tasks, including adaptive quantile regression, modal regression, tabular prediction, image captioning, and question answering, demonstrating its versatility and effectiveness across domains.