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Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 14276)


Active learning aims to reduce the amount of labeled data while maximizing machine learning models’ performances. Currently, there is sparse research on the potential of an optimal active learning strategy. Therefore, we propose a non-myopic oracle policy that accesses the true labels of the data pool to approximate an optimal active learning strategy. We evaluate how the hyperparameters of this oracle policy influence its performance and empirically demonstrate that it is an upper baseline for common active learning strategies while being faster than a state-of-the-art oracle policy. For the sake of reproducibility, all the code related to our research is publicly available on our GitHub repository at


  • Active Learning
  • Oracle Policy
  • Classification

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Correspondence to Christoph Sandrock .

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Sandrock, C., Herde, M., Kottke, D., Sick, B. (2023). Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham.

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  • Print ISBN: 978-3-031-45274-1

  • Online ISBN: 978-3-031-45275-8

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