Quantifying Deep Learning Model Uncertainty in Conformal Prediction
This addresses the need for reliable uncertainty estimation in medical AI, but it is incremental as it builds on existing conformal prediction frameworks.
The paper tackles the problem of quantifying model uncertainty in conformal prediction for deep learning, proposing a probabilistic approach that provides certified boundaries for uncertainty, enabling comparison with methods like Bayesian and Evidential approaches.
Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoretical foundations. We propose a probabilistic approach in quantifying the model uncertainty derived from the produced prediction sets in conformal prediction and provide certified boundaries for the computed uncertainty. By doing so, we allow model uncertainty measured by CP to be compared by other uncertainty quantification methods such as Bayesian (e.g., MC-Dropout and DeepEnsemble) and Evidential approaches.