mlsanityData to 7 Oct 2026

Active Learning: Problem Settings and Recent Developments

arXiv 2012.04225 ↗First posted 8 Dec 2020cs.LG stat.MLcited by 23

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Abstract (arXiv)

In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high precision at a limited cost through the adaptive selection of samples for labeling. This paper explains the basic problem settings of active learning and recent research trends. In particular, research on learning acquisition functions to select samples from the data for labeling, theoretical work on active learning algorithms, and stopping criteria for sequential data acquisition are highlighted. Application examples for material development and measurement are introduced.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Dec 2020.

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Citing papers per year: 2021: 1; 2022: 7; 2023: 5; 2024: 6; 2025: 4.

23 papers.

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  1. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  2. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  3. 2025From Selection to Generation: A Survey of LLM-based Active Learningcited by 18
  4. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  5. 2022Active Learning by Query by Committee with Robust Divergencescited by 9
  6. 2021Stopping Criterion for Active Learning Based on Error Stabilitycited by 6
  7. 2023Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classificationcited by 5
  8. 2023Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detectioncited by 4
  9. 2022Cost-effective Framework for Gradual Domain Adaptation with Multifidelitycited by 3
  10. 2022Impact of Stop Sets on Stopping Active Learning for Text Classificationcited by 2
  11. 2022Centerpoints Are All You Need in Overhead Imagerycited by 2
  12. 2024Improving Uncertainty Sampling with Bell Curve Weight Functioncited by 2
11 more
  1. 2025Info-Coevolution: An Efficient Framework for Data Model Coevolutioncited by 2
  2. 2022Improving Probabilistic Models in Text Classification via Active Learningcited by 1
  3. 2024Statistical curriculum learning: An elimination algorithm achieving an oracle riskcited by 1
  4. 2024Solving 0-1 Integer Programs with Unknown Knapsack Constraints Using Membership Oraclescited by 1
  5. 2022Active Discrimination Learning for Gaussian Process Modelscited by 0
  6. 2023Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Modelcited by 0
  7. 2023Applied metamodelling for ATM performance simulationscited by 0
  8. 2024Improve Cost Efficiency of Active Learning over Noisy Datasetcited by 0
  9. 2024Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policiescited by 0
  10. 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancementcited by 0
  11. 2025Cost-effective Reduced-Order Modeling via Bayesian Active Learningcited by 0