# Conformalized Multimodal Uncertainty Regression and Reasoning

arXiv: https://arxiv.org/abs/2309.11018 · HTML: https://mlsanity.com/paper/2309.11018/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Bayesian Neural Networks: An Introduction and Survey](https://mlsanity.com/paper/2006.12024/) (2020)
- [Training Uncertainty-Aware Classifiers with Conformalized Deep Learning](https://mlsanity.com/paper/2205.05878/) (2022)
- [Improving Conditional Coverage via Orthogonal Quantile Regression](https://mlsanity.com/paper/2106.00394/) (2021)
- [Self-Supervised Deep Visual Odometry with Online Adaptation](https://mlsanity.com/paper/2005.06136/) (2020)
- [Lightweight, Uncertainty-Aware Conformalized Visual Odometry](https://mlsanity.com/paper/2303.02207/) (2023)
- [Quantifying Deep Learning Model Uncertainty in Conformal Prediction](https://mlsanity.com/paper/2306.00876/) (2023)
- [Conformalized Unconditional Quantile Regression](https://mlsanity.com/paper/2304.01426/) (2023)
- [MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence](https://mlsanity.com/paper/2111.07125/) (2021)
- [Nonparametric Quantile Regression: Non-Crossing Constraints and Conformal Prediction](https://mlsanity.com/paper/2210.10161/) (2022)

## Built on by (most cited first, 6 of 6)

- [Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception](https://mlsanity.com/paper/2502.07255/) (2025)
- [INTACT: Inducing Noise Tolerance through Adversarial Curriculum Training for LiDAR-based Safety-Critical Perception and Autonomy](https://mlsanity.com/paper/2502.01896/) (2025)
- [Intelligent Sensing-to-Action for Robust Autonomy at the Edge: Opportunities and Challenges](https://mlsanity.com/paper/2502.02692/) (2025)
- [Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templates](https://mlsanity.com/paper/2312.07434/) (2023)
- [Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction](https://mlsanity.com/paper/2604.07198/) (2026)
- [Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration](https://mlsanity.com/paper/2605.03820/) (2026)

## Abstract (arXiv)

This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning regressor. We specifically discuss its application for visual odometry (VO), where environmental features such as flying domain symmetries and sensor measurements under ambiguities and occlusion can result in multimodal uncertainties. Our simulation results show that uncertainty estimates in our framework adapt sample-wise against challenging operating conditions such as pronounced noise, limited training data, and limited parametric size of the prediction model. We also develop a reasoning framework that leverages these robust uncertainty estimates and incorporates optical flow-based reasoning to improve prediction prediction accuracy. Thus, by appropriately accounting for predictive uncertainties of data-driven learning and closing their estimation loop via rule-based reasoning, our methodology consistently surpasses conventional deep learning approaches on all these challenging scenarios--pronounced noise, limited training data, and limited model size-reducing the prediction error by 2-3x.