Web: http://arxiv.org/abs/2111.03740

June 20, 2022, 1:11 a.m. | Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric Xing

cs.LG updates on arXiv.org arxiv.org

Machine learning has demonstrated remarkable prediction accuracy over i.i.d
data, but the accuracy often drops when tested with data from another
distribution. In this paper, we aim to offer another view of this problem in a
perspective assuming the reason behind this accuracy drop is the reliance of
models on the features that are not aligned well with how a data annotator
considers similar across these two datasets. We refer to these features as
misaligned features. We extend the conventional …

arxiv cross features human learning lg models

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