mlsanityData to 13 Oct 2026

Learning Algorithms for Active Learning

arXiv 1708.00088 ↗First posted 31 Jul 2017cs.LGcited by 30

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Abstract (arXiv)

We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a method for constructing prediction functions from labeled training sets. Our model uses the item selection heuristic to gather labeled training sets from which to construct prediction functions. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.

Key facts. Cited by 30 papers in the map (data through 13 Oct 2026). First posted on arXiv 31 Jul 2017.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 5; 2024: 3; 2025: 2; 2026: 2.

30 papers.

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  1. 2021Active Learning for Domain Adaptation: An Energy-Based Approachcited by 53
  2. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  3. 2021How to Train Your MAML to Excel in Few-Shot Classificationcited by 22
  4. 2022Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chesscited by 18
  5. 2021Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testingcited by 17
  6. 2021On Data Efficiency of Meta-learningcited by 16
  7. 2022Few-shot learning for medical text: A systematic reviewcited by 13
  8. 2022Asking for Knowledge: Training RL Agents to Query External Knowledge Using Languagecited by 10
  9. 2023On the Limitations of Simulating Active Learningcited by 9
  10. 2021How to distribute data across tasks for meta-learning?cited by 7
  11. 2021A Survey on Active Deep Learning: From Model-driven to Data-drivencited by 4
  12. 2021MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learningcited by 4
18 more
  1. 2021ImitAL: Learning Active Learning Strategies from Synthetic Datacited by 3
  2. 2022Budget-aware Few-shot Learning via Graph Convolutional Networkcited by 3
  3. 2022Deep reinforced active learning for multi-class image classificationcited by 3
  4. 2023Active Continual Learning: On Balancing Knowledge Retention and Learnabilitycited by 3
  5. 2023Learning to Rank for Active Learning via Multi-Task Bilevel Optimizationcited by 3
  6. 2024Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guaranteescited by 3
  7. 2024Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Modelscited by 3
  8. 2023Controlling Federated Learning for Covertnesscited by 2
  9. 2024Active Reinforcement Learning Strategies for Offline Policy Improvementcited by 2
  10. 2026Performance-guided Reinforced Active Learning for Object Detectioncited by 2
  11. 2022ImitAL: Learned Active Learning Strategy on Synthetic Datacited by 1
  12. 2021Active Learning for Argument Mining: A Practical Approachcited by 0
  13. 2021Fair Active Learning: Solving the Labeling Problem in Insurancecited by 0
  14. 2022Active Learning for Regression with Aggregated Outputscited by 0
  15. 2023Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detectioncited by 0
  16. 2025Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Explorationcited by 0
  17. 2025Thompson Sampling-Based Learning and Control for Unknown Dynamic Systemscited by 0
  18. 2026ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentationcited by 0