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Efficient Active Learning with Abstention. (arXiv:2204.00043v1 [stat.ML])
April 4, 2022, 1:11 a.m. | Yinglun Zhu, Robert Nowak
cs.LG updates on arXiv.org arxiv.org
The goal of active learning is to achieve the same accuracy achievable by
passive learning, while using much fewer labels. Exponential savings in label
complexity are provably guaranteed in very special cases, but fundamental lower
bounds show that such improvements are impossible in general. This suggests a
need to explore alternative goals for active learning. Learning with abstention
is one such alternative. In this setting, the active learning algorithm may
abstain from prediction in certain cases and incur an error …
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