mlsanityData to 7 Oct 2026

Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing

arXiv 2310.06306 ↗First posted 10 Oct 2023cs.LG stat.MLcited by 5

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Abstract (arXiv)

It is challenging but important to save annotation efforts in streaming data acquisition to maintain data quality for supervised learning base learners. We propose an ensemble active learning method to actively acquire samples for annotation by contextual bandits, which is will enforce the exploration-exploitation balance and leading to improved AI modeling performance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 4 of its 84 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

4 of 84 references.

References · 4

  1. 2013Bandits with Knapsackscited by 163
  2. 2021Poisoning the Unlabeled Dataset of Semi-Supervised Learningcited by 44
  3. 2019Better Data Labelling with EMBLEM (and how that Impacts Defect Prediction)cited by 13
  4. 2019AKM2D : An Adaptive Framework for Online Sensing and Anomaly Quantificationcited by 3

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Citing papers per year: 2024: 2; 2025: 3.

5 papers.