# Multimodal data integration and cross-modal querying via orchestrated approximate message passing

arXiv: https://arxiv.org/abs/2407.19030 · HTML: https://mlsanity.com/paper/2407.19030/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2024.

## Built on by (most cited first, 5 of 5)

- [Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables](https://mlsanity.com/paper/2505.12473/) (2025)
- [Computational Thresholds in Multi-Modal Learning via the Spiked Matrix-Tensor Model](https://mlsanity.com/paper/2506.02664/) (2025)
- [Multi-layer Cross-attention is Provably Optimal for Multi-modal In-context Learning](https://mlsanity.com/paper/2602.04872/) (2026)
- [Clustering by Denoising: Latent plug-and-play diffusion for single-cell data](https://mlsanity.com/paper/2510.22835/) (2025)
- [DAIF: A Data-Driven Intermediate Fusion Framework for Multimodal Supervised Learning via Approximate Message Passing](https://mlsanity.com/paper/2608.02769/) (2026)

## Abstract (arXiv)

The need for multimodal data integration arises naturally when multiple complementary sets of features are measured on the same sample. Under a dependent multifactor model, we develop a fully data-driven orchestrated approximate message passing algorithm for integrating information across these feature sets to achieve statistically optimal signal recovery. In practice, these reference data sets are often queried later by new subjects that are only partially observed. Leveraging on asymptotic normality of estimates generated by our data integration method, we further develop an asymptotically valid prediction set for the latent representation of any such query subject. We demonstrate the prowess of both the data integration and the prediction set construction algorithms on both synthetic examples and real world single-cell datasets.