# Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction

arXiv: https://arxiv.org/abs/2308.09647 · HTML: https://mlsanity.com/paper/2308.09647/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 18 Aug 2023.

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

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation](https://mlsanity.com/paper/1810.07884/) (2018)
- [Importance-Driven Deep Learning System Testing](https://mlsanity.com/paper/2002.03433/) (2020)
- [UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation](https://mlsanity.com/paper/2306.16549/) (2023)
- [Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognition](https://mlsanity.com/paper/1901.10139/) (2019)

## Built on by (most cited first, 10 of 10)

- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction](https://mlsanity.com/paper/2401.06421/) (2024)
- [Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding](https://mlsanity.com/paper/2405.18180/) (2024)
- [Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning](https://mlsanity.com/paper/2506.05937/) (2025)
- [Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows](https://mlsanity.com/paper/2512.00835/) (2025)
- [A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification](https://mlsanity.com/paper/2605.16383/) (2026)
- [Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning](https://mlsanity.com/paper/2510.18485/) (2025)
- [Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series](https://mlsanity.com/paper/2605.26569/) (2026)
- [Estimating Uncertainty in Galaxy Morphology Classification](https://mlsanity.com/paper/2608.08398/) (2026)
- [Localisation-Aware Uncertainty for Pretrained Object Detection](https://mlsanity.com/paper/2610.01409/) (2026)

## Abstract (arXiv)

Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models. Uncertainty quantification (UQ) methods estimate the model's confidence per prediction, informing decision-making by considering the effect of randomness and model misspecification. Despite the advances of state-of-the-art UQ methods, they are computationally expensive or produce conservative prediction sets/intervals. We introduce MC-CP, a novel hybrid UQ method that combines a new adaptive Monte Carlo (MC) dropout method with conformal prediction (CP). MC-CP adaptively modulates the traditional MC dropout at runtime to save memory and computation resources, enabling predictions to be consumed by CP, yielding robust prediction sets/intervals. Throughout comprehensive experiments, we show that MC-CP delivers significant improvements over advanced UQ methods, like MC dropout, RAPS and CQR, both in classification and regression benchmarks. MC-CP can be easily added to existing models, making its deployment simple.