Scalable Bayesian Cooperative Learning for Multimodal Integration

Music City Center 

Multimodal integration has made significant strides in recent years, evolving from early to late fusion approaches and achieving notable performance gains over single-view methods. Substantial questions remain, however, particularly at the intersection of dependence-aware multimodal integration and uncertainty-aware multiview feature selection - both challenging for current integration paradigms. To bridge these longstanding gaps, we propose a scalable Bayesian cooperative learning method, BayesCOOP, which combines jittered group spike-and-slab L1 regularization with intermediate fusion. For uncertainty quantification, BayesCOOP employs the Bayesian bootstrap to generate approximate posterior samples via maximum a posteriori (MAP) estimation on jittered, resampled datasets. This approach inherits strong theoretical guarantees, including posterior contraction at near-optimal rates in sparse, high-dimensional regimes, while enabling scalable pseudo-posterior inference. As one of the first uncertainty-aware multimodal approaches in the field, BayesCOOP significantly outperforms state-of-the-art approaches, including early, late, and intermediate fusion. Analyzing two published multimodal datasets using BayesCOOP, we show that it can be up to 20 times more powerful than existing methods and disclose multimodal discoveries that otherwise cannot be revealed by existing approaches. Our open-source software is publicly available.

Transfer Learning

Foundation Model

Microbiome

Machine Learning

Multi-omics

Metabolomics