A novel Deep Learning approach for one-step Conformal Prediction approximation
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Deep Learning predictions with measurable confidence are increasingly desirable for real-world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is a versatile solution that automatically guarantees a maximum error rate. However, CP suffers from computational inefficiencies that limit its application to large-scale datasets. In this paper, we propose a novel conformal loss function that approximates the traditionally two-step CP approach in a single step. By evaluating and penalising deviations from the stringent expected CP output distribution, a Deep Learning model may learn the direct relationship between input data and conformal p-values. Our approach achieves significant training time reductions up to 86 approximation variant. In terms of approximate validity and predictive efficiency, we carry out a comprehensive empirical evaluation to show our novel loss function's competitiveness with ACP for binary and multi-class classification on the well-established MNIST dataset.
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