mlsanityData to 7 Oct 2026

Pool-Based Sequential Active Learning for Regression

arXiv 1805.04735 ↗First posted 12 May 2018cs.LG stat.MLcited by 33

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Abstract (arXiv)

Active learning is a machine learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential active learning for regression (ALR). We first propose three essential criteria that an ALR approach should consider in selecting the most useful unlabeled samples: informativeness, representativeness, and diversity, and compare four existing ALR approaches against them. We then propose a new ALR approach using passive sampling, which considers both the representativeness and the diversity in both the initialization and subsequent iterations. Remarkably, this approach can also be integrated with other existing ALR approaches in the literature to further improve the performance. Extensive experiments on 11 UCI, CMU StatLib, and UFL Media Core datasets from various domains verified the effectiveness of our proposed ALR approaches.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2018.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 6; 2024: 7; 2025: 5; 2026: 4.

33 papers.

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  1. 2022A Framework and Benchmark for Deep Batch Active Learning for Regressioncited by 47
  2. 2022Bayesian Active Learning with Fully Bayesian Gaussian Processescited by 26
  3. 2024Active Learning for Neural PDE Solverscited by 22
  4. 2023Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goalcited by 17
  5. 2024Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choicescited by 12
  6. 2022Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applicationscited by 11
  7. 2022Active Learning for Regression by Inverse Distance Weightingcited by 9
  8. 2022Image Quality Assessment: Integrating Model-Centric and Data-Centric Approachescited by 5
  9. 2025Active Learning with Selective Time-Step Acquisition for PDEscited by 4
  10. 2022Bucketized Active Sampling for Learning ACOPFcited by 3
  11. 2024Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimationcited by 3
  12. 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspectivecited by 3
21 more
  1. 2022PyRelationAL: a python library for active learning research and developmentcited by 2
  2. 2023Active Cost-aware Labeling of Streaming Datacited by 2
  3. 2023Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filtercited by 2
  4. 2023Streaming Active Learning for Regression Problems Using Regression via Classificationcited by 2
  5. 2024"Forgetting" in Machine Learning and Beyond: A Surveycited by 2
  6. 2024Efficient Biological Data Acquisition through Inference Set Designcited by 2
  7. 2025Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasetscited by 2
  8. 2021Distribution Matching for Machine Teachingcited by 1
  9. 2023Does Deep Active Learning Work in the Wild?cited by 1
  10. 2023Human in the AI loop via xAI and Active Learning for Visual Inspectioncited by 1
  11. 2024Sample Efficient Robot Learning in Supervised Effect Prediction Taskscited by 1
  12. 2025Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxiescited by 1
  13. 2026MDS-VQA: Model-Informed Data Selection for Video Quality Assessmentcited by 1
  14. 2021Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inferencecited by 0
  15. 2021Optimal Sampling Density for Nonparametric Regressioncited by 0
  16. 2022From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)cited by 0
  17. 2024Classification Tree-based Active Learning: A Wrapper Approachcited by 0
  18. 2025Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learningcited by 0
  19. 2026Feature Weighting Improves Pool-Based Sequential Active Learning for Regressioncited by 0
  20. 2026TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learningcited by 0
  21. 2026How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limitcited by 0