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A Meta-Learning Approach to One-Step Active-Learning

Abstract : We consider the problem of learning when obtaining the training labels is costly, which is usually tackled in the literature using active-learning techniques. These approaches provide strategies to choose the examples to label before or during training. These strategies are usually based on heuristics or even theoretical measures, but are not learned as they are directly used during training. We design a model which aims at learning active-learning strategies using a meta-learning setting. More specifically, we consider a pool-based setting, where the system observes all the examples of the dataset of a problem and has to choose the subset of examples to label in a single shot. Experiments show encouraging results.
Keywords : Budget Learning
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Contributor : Thierry Artières <>
Submitted on : Wednesday, January 24, 2018 - 8:40:59 AM
Last modification on : Friday, January 8, 2021 - 5:34:10 PM
Long-term archiving on: : Thursday, May 24, 2018 - 2:22:25 PM


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  • HAL Id : hal-01691472, version 1


Gabriella Contardo, Ludovic Denoyer, Thierry Artières. A Meta-Learning Approach to One-Step Active-Learning. International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, Sep 2017, Skopje, Macedonia. pp.28-40. ⟨hal-01691472⟩



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