Generative Distribution Prediction: A Unified Approach to Multimodal Learning

2025-02-10

AI Summary

This paper addresses the challenge of jointly modeling heterogeneous multimodal data (tabular, textual, and visual) and achieving high-accuracy prediction. We propose Generative Distribution Prediction (GDP), a novel framework centered on conditional diffusion models that performs point prediction via generative distribution estimation—introducing the first unified distribution-level prediction paradigm. Theoretically, we prove its statistical consistency under the diffusion model setting. GDP is model-agnostic: it accommodates arbitrary generative architectures and loss functions, and natively supports cross-modal representation alignment and transfer learning. Extensive experiments demonstrate state-of-the-art performance across four diverse tasks—tabular forecasting, question answering, image captioning, and quantile regression—validating both its generality and effectiveness.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Unified multimodal learning framework
• Enhances predictive accuracy
• Supports diverse data types

Innovation

• Generative Distribution Prediction framework
• Leverages multimodal synthetic data generation
• Supports transfer learning adaptation