Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction

• URL: http://arxiv.org/abs/2308.09647v2
• Date: Mon, 22 Jan 2024 11:14:39 GMT
• Title: Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
• Authors: Daniel Bethell, Simos Gerasimou, Radu Calinescu
• Abstract summary: Uncertainty quantification (UQ) methods estimate the model's confidence per prediction. We introduce MC-CP, a novel hybrid UQ method that combines a new adaptive Monte Carlo (MC) dropout method with conformal prediction (CP) We show that MC-CP delivers significant improvements over advanced UQ methods, like MC dropout, RAPS and CQR, both in classification and regression benchmarks.
• Score: 6.86690482279886
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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