Generative Distribution Prediction: A Unified Approach to Multimodal Learning
This work addresses the problem of integrating heterogeneous data types for predictive analytics in various application domains, providing a solution for researchers and practitioners working with multimodal data.
The authors tackled the problem of accurate prediction with multimodal data by introducing Generative Distribution Prediction (GDP), a novel framework that leverages multimodal synthetic data generation, resulting in enhanced predictive performance across structured and unstructured modalities. GDP demonstrated its versatility and effectiveness across four diverse domains.
Accurate prediction with multimodal data-encompassing tabular, textual, and visual inputs or outputs-is fundamental to advancing analytics in diverse application domains. Traditional approaches often struggle to integrate heterogeneous data types while maintaining high predictive accuracy. We introduce Generative Distribution Prediction (GDP), a novel framework that leverages multimodal synthetic data generation-such as conditional diffusion models-to enhance predictive performance across structured and unstructured modalities. GDP is model-agnostic, compatible with any high-fidelity generative model, and supports transfer learning for domain adaptation. We establish a rigorous theoretical foundation for GDP, providing statistical guarantees on its predictive accuracy when using diffusion models as the generative backbone. By estimating the data-generating distribution and adapting to various loss functions for risk minimization, GDP enables accurate point predictions across multimodal settings. We empirically validate GDP on four supervised learning tasks-tabular data prediction, question answering, image captioning, and adaptive quantile regression-demonstrating its versatility and effectiveness across diverse domains.