# Conformal Prediction for Multimodal Regression

Source: [Conformal Prediction for Multimodal Regression](<https://www.sota2.com/research/paper/68fa75701e209131cea67eb0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-16T05:00:12.406Z

Authors: Alexis Bose, Jonathan Ethier, Paul Guinand

Published: 2024-10-25T15:56:39.000Z

## Abstract

This paper introduces multimodal conformal regression. Traditionally confined to scenarios with solely numerical input features, conformal prediction is now extended to multimodal contexts through our methodology, which harnesses internal features from complex neural network architectures processing images and unstructured text. Our findings highlight the potential for internal neural network features, extracted from convergence points where multimodal information is combined, to be used by conformal prediction to construct prediction intervals (PIs). This capability paves new paths for deploying conformal prediction in domains abundant with multimodal data, enabling a broader range of problems to benefit from guaranteed distribution-free uncertainty quantification.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Rental Price Regression on Melbourne Airbnb (test)](<https://www.sota2.com/research/sota/rental-price-regression-on-melbourne-airbnb-test.md>): Rental price regression; Melbourne Airbnb (test); Mean PI Width: 175.1.

- [RSRP Prediction on DTU RSRP](<https://www.sota2.com/research/sota/rsrp-prediction-on-dtu-rsrp.md>): RSRP Prediction; DTU RSRP; Mean PI Width (dB): 20.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2410.19653>)

[PDF](<https://arxiv.org/pdf/2410.19653.pdf>)