Training Uncertainty-Aware Classifiers with Conformalized Deep Learning

Bat-Sheva Einbinder, Yaniv Romano, Matteo Sesia, Yanfei Zhou

Keywords: Deep learning, Uncertainty, Conformal inference, Multi-class classification, Overfitting, Confidence.

TL;DR: This paper develops a novel loss function and learning algorithm for training uncertainty-aware deep neural classifiers that can lead to smaller conformal prediction sets with more reliable coverage compared to standard state-of-the-art techniques.

Abstract: Deep neural networks are powerful tools to detect hidden patterns in data and leverage them to make predictions, but they are not designed to understand uncertainty and estimate reliable probabilities. In particular, they tend to be overconfident. We begin to address this problem in the context of multi-class classification by developing a novel training algorithm producing models with more dependable uncertainty estimates, without sacrificing predictive power. The idea is to mitigate overconfidence by minimizing a loss function, inspired by advances in conformal inference, that quantifies model uncertainty by carefully leveraging hold-out data. Experiments with synthetic and real data demonstrate this method can lead to smaller conformal prediction sets with higher conditional coverage, after exact calibration with hold-out data, compared to state-of-the-art alternatives.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2205.05878/code)