May 9, 2024, 4:44 a.m. | G\'abor Lugosi, Marcos Matabuena

stat.ML updates on

arXiv:2405.05110v1 Announce Type: cross
Abstract: This paper introduces a novel uncertainty quantification framework for regression models where the response takes values in a separable metric space, and the predictors are in a Euclidean space. The proposed algorithms can efficiently handle large datasets and are agnostic to the predictive base model used. Furthermore, the algorithms possess asymptotic consistency guarantees and, in some special homoscedastic cases, we provide non-asymptotic guarantees. To illustrate the effectiveness of the proposed uncertainty quantification framework, we use …

abstract algorithms arxiv datasets framework large datasets novel paper predictive quantification regression space spaces type uncertainty values

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