mlsanityData to 7 Oct 2026

Learning Active Learning from Data

arXiv 1703.03365 ↗First posted 9 Mar 2017cs.LGcited by 100

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Abstract (arXiv)

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains.

Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 19; 2023: 22; 2024: 18; 2025: 18; 2026: 6.

100 papers.

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  1. 2021Learning 3D Semantic Segmentation with only 2D Image Supervisioncited by 60
  2. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  3. 2021SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistencycited by 47
  4. 2022OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learningcited by 37
  5. 2024Active Evaluation Acquisition for Efficient LLM Benchmarkingcited by 33
  6. 2023A Survey on Dataset Distillation: Approaches, Applications and Future Directionscited by 32
  7. 2021On Training Instance Selection for Few-Shot Neural Text Generationcited by 25
  8. 2021Boosting Active Learning via Improving Test Performancecited by 19
  9. 2023Learning the What and How of Annotation in Video Object Segmentationcited by 19
  10. 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18
  11. 2023Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisitioncited by 17
  12. 2023Towards Active Learning for Action Spotting in Association Football Videoscited by 14
38 more
  1. 2024AAPL: Adding Attributes to Prompt Learning for Vision-Language Modelscited by 14
  2. 2021Automated Deep Learning: Neural Architecture Search Is Not the Endcited by 13
  3. 2022Deep Active Learning with Noise Stabilitycited by 13
  4. 2023DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Designcited by 12
  5. 2024On the Limitations of Large Language Models (LLMs): False Attributioncited by 12
  6. 2022One Size Does Not Fit All: The Case for Personalised Word Complexity Modelscited by 10
  7. 2022Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learningcited by 10
  8. 2021Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processescited by 9
  9. 2021Learning an Explicit Hyperparameter Prediction Function Conditioned on Taskscited by 9
  10. 2022Active Learning by Query by Committee with Robust Divergencescited by 9
  11. 2023On the Limitations of Simulating Active Learningcited by 9
  12. 2024LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classificationcited by 8
  13. 2022Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classificationcited by 7
  14. 2021Stopping Criterion for Active Learning Based on Error Stabilitycited by 6
  15. 2021Multi-Domain Active Learning: Literature Review and Comparative Studycited by 6
  16. 2022FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clusteringcited by 6
  17. 2023You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentationcited by 6
  18. 2025Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Networkcited by 6
  19. 2021Active Learning for Network Traffic Classification: A Technical Studycited by 5
  20. 2022Competition over data: how does data purchase affect users?cited by 5
  21. 2022Onception: Active Learning with Expert Advice for Real World Machine Translationcited by 5
  22. 2022Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identificationcited by 5
  23. 2023Deep Active Learning in the Presence of Label Noise: A Surveycited by 5
  24. 2021A Survey on Active Deep Learning: From Model-driven to Data-drivencited by 4
  25. 2021TDACNN: Target-domain-free Domain Adaptation Convolutional Neural Network for Drift Compensation in Gas Sensorscited by 4
  26. 2023A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Predictioncited by 4
  27. 2023Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detectioncited by 4
  28. 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
  29. 2024Pseudo-triplet Guided Few-shot Composed Image Retrievalcited by 4
  30. 2021ImitAL: Learning Active Learning Strategies from Synthetic Datacited by 3
  31. 2021Multi-View Active Learning for Short Text Classification in User-Generated Datacited by 3
  32. 2022Budget-aware Few-shot Learning via Graph Convolutional Networkcited by 3
  33. 2022Deep reinforced active learning for multi-class image classificationcited by 3
  34. 2022Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarioscited by 3
  35. 2023REAL: A Representative Error-Driven Approach for Active Learningcited by 3
  36. 2023Learning to Rank for Active Learning via Multi-Task Bilevel Optimizationcited by 3
  37. 2024Distribution Guided Active Feature Acquisitioncited by 3
  38. 2025OmViD: Omni-supervised active learning for video action detectioncited by 3

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