# Beyond Disagreement-Based Agnostic Active Learning

@inproceedings{Zhang2014BeyondDA, title={Beyond Disagreement-Based Agnostic Active Learning}, author={Chicheng Zhang and Kamalika Chaudhuri}, booktitle={NIPS}, year={2014} }

We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which…

## 80 Citations

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### Selectively Inhibiting Learning Bias for Active Sampling

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A simple hybrid strategy and a visualization tool called ranking curves, which allowed to see clearly when the presence of a learner was possibly detrimental and was successfully compared to its counterpart in the literature, to pure agnostic strategies and to the usual baseline of the field.

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