May 8, 2024, 4:42 a.m. | Pei Liu, Luping Ji

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.04405v1 Announce Type: new
Abstract: Uncertainty estimation (UE), as an effective means of quantifying predictive uncertainty, is crucial for safe and reliable decision-making, especially in high-risk scenarios. Existing UE schemes usually assume that there are completely-labeled samples to support fully-supervised learning. In practice, however, many UE tasks often have no sufficiently-labeled data to use, such as the Multiple Instance Learning (MIL) with only weak instance annotations. To bridge this gap, this paper, for the first time, addresses the weakly-supervised issue …

abstract arxiv cs.lg data decision however instance making practice predictive residual risk safe samples supervised learning support tasks type uncertainty weakly-supervised

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