IALE: Imitating Active Learner Ensembles

Christoffer Löffler, Christopher Mutschler

Keywords: active learning, imitating learning, ensembles

Abstract: Active learning (AL) prioritizes the labeling of the most informative data samples. However, the performance of AL heuristics depends on the structure of the underlying classifier model and the data. We propose an imitation learning scheme that imitates the selection of the best expert heuristic at each stage of the AL cycle in a batch-mode pool-based setting. We use DAGGER to train the policy on a dataset and later apply it to datasets from similar domains. With multiple AL heuristics as experts, the policy is able to reflect the choices of the best AL heuristics given the current state of the AL process. Our experiment on well-known datasets show that we both outperform state of the art imitation learners and heuristics.

One-sentence Summary: IALE uses imitation learning to learn a policy for pool-based active learning (AL) that imitates a set of hard-coded experts that all together outperform state of the art AL baselines

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Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2007.04637/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=cJIoANmPrS