Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification

arXiv:2403.10368v1 Announce Type: cross
Abstract: Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we …

algorithm algorithms arxiv classification cs.cr cs.lg design framework good machine machine learning practice predictions stat.ml