June 6, 2024, 4:44 a.m. | Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03258v1 Announce Type: cross
Abstract: Constructing valid prediction intervals rather than point estimates is a well-established approach for uncertainty quantification in the regression setting. Models equipped with this capacity output an interval of values in which the ground truth target will fall with some prespecified probability. This is an essential requirement in many real-world applications where simple point predictions' inability to convey the magnitude and frequency of errors renders them insufficient for high-stakes decisions. Quantile regression is a leading approach …

abstract arxiv capacity cs.lg interval noise output prediction probability quantification quantile regression stat.ml truth type uncertainty values will

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