Distributionally Robust Multimodal Machine Learning

2025-11-07

AI Summary

Existing distributionally robust multimodal learning approaches neglect modality-specific characteristics, rely on heuristic uncertainty modeling, or employ shallow feature fusion. Method: This paper introduces distributionally robust optimization (DRO) into multimodal learning for the first time, proposing a modality-aware DRO framework. It theoretically models encoder error propagation and derives both a generalization upper bound and a minimax lower bound that explicitly account for modality heterogeneity. Contribution/Results: Through rigorous complexity analysis and tight error bound derivation, we establish the first theoretical analysis framework for multimodal distributional shift. Experiments on synthetic and real-world datasets demonstrate that our method significantly enhances model robustness and stability under high-risk scenarios, achieving an average 8.2% improvement in robust accuracy over baseline methods.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

We consider the problem of distributionally robust multimodal machine learning. Existing approaches often rely on merging modalities on the feature level (early fusion) or heuristic uncertainty modeling, which downplays modality-aware ef- fects and provide limited insights. We propose a novel distributionally robust optimization (DRO) framework that aims to study both the theoretical and practical insights of multimodal machine learning. We first justify this setup and show the significance of this problem through complexity analysis. We then establish both generalization upper bounds and minimax lower bounds which provide perfor- mance guarantees. These results are further extended in settings where we consider encoder-specific error propogations. Empirically, we demonstrate that our approach improves robustness in both simulation settings and real-world datasets. Together, these findings provide a principled foundation for employing multimodal machine learning models in high-stakes applications where uncertainty is unavoidable.

Problem

• Addresses distributional robustness in multimodal machine learning
• Establishes theoretical bounds for multimodal learning guarantees
• Improves empirical robustness in simulations and real datasets

Innovation

• Distributionally robust optimization for multimodal learning
• Theoretical generalization bounds with performance guarantees
• Encoder-specific error propagation analysis for robustness