@inproceedings{62059f2d0c3242659a41013927b21bfa,
title = "An Effective Method for Identifying Unknown Unknowns with Noisy Oracle",
abstract = "Unknown Unknowns (UUs) are referred to the error predictions that with high confidence. The identifying of the UUs is important to understand the limitation of predictive models. Some proposed solutions are effective in such identifying. All of them assume there is a perfect Oracle to return the correct labels of the UUs. However, it is not practical since there is no perfect Oracle in real world. Even experts will make mistakes in UUs labelling. Such errors will lead to the terrible consequence since fake UUs will mislead the existing algorithms and reduce their performance. In this paper, we identify the impact of noisy Oracle and propose a UUs identifying algorithm that can be adapted to the setting of noisy Oracle. Experimental results demonstrate the effectiveness of our proposed method.",
keywords = "Active learning, Model diagnosis, Uncertainty AI, Unknown Unknowns",
author = "Bo Zheng and Xin Lin and Yanghua Xiao and Jing Yang and Liang He",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 26th International Conference on Case-Based Reasoning, ICCBR 2018 ; Conference date: 09-07-2018 Through 12-07-2018",
year = "2018",
doi = "10.1007/978-3-030-01081-2\_32",
language = "英语",
isbn = "9783030010805",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "480--495",
editor = "Cox, \{Michael T.\} and Peter Funk and Shahina Begum",
booktitle = "Case-Based Reasoning Research and Development - 26th International Conference, ICCBR 2018, Proceedings",
address = "德国",
}