Methodological study demonstrates enhanced predictive performance across diverse multimodal datasets, indicating generative synthetic data can unify structured and unstructured learning.
Key Points
To develop a unified, model-agnostic predictive framework capable of integrating heterogeneous structured and unstructured multimodal data while maintaining high predictive accuracy.
Formulated Generative Distribution Prediction (GDP) to generate high-fidelity synthetic data from conditional target distributions using expressive generative architectures such as conditional diffusion models.
Integrated loss-adapted risk minimization and established theoretical statistical bounds for predictive accuracy under diffusion-based conditional distribution estimation.
Evaluated empirical performance across diverse supervised tasks, including adaptive quantile regression, modal regression, tabular prediction, image captioning, and question answering.
Provided theoretical statistical guarantees confirming bounded predictive error when conditional diffusion models serve as the generative distribution backbone.
Demonstrated qualitatively superior accuracy and versatility across multimodal benchmarks relative to conventional predictive baselines, effectively supporting transfer learning for domain adaptation.