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Meta-Learning for Batch Mode Active Learning
Sachin Ravi, Hugo Larochelle
Feb 12, 2018 (modified: Feb 12, 2018)ICLR 2018 Workshop Submissionreaders: everyone
Abstract:Active learning involves selecting unlabeled data items to label in order to best improve an existing classifier. In most applications, batch mode active learning, where a set of items is picked all at once to be labeled and then used to re-train the classifier, is most feasible because it does not require the model to be re-trained after each individual selection and makes most efficient use of human labor for annotation. In this work, we explore using meta-learning to learn an active learning algorithm that selects the best set of unlabeled items to label given a classifier trained on a small training set. Our experiments show that our learned active learning algorithm is able to construct labeled sets that improve a classifier better than commonly used heuristics.
TL;DR:We explore using meta-learning to learn an active learning algorithm that selects the best set of unlabeled items to label given a classifier trained on a small training set.
Keywords:meta-learning, active learning
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