mlsanityData to 7 Oct 2026

Active Learning for Regression Using Greedy Sampling

arXiv 1808.04245 ↗First posted 8 Aug 2018cs.LG cs.AI stat.MLcited by 26

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Abstract (arXiv)

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2018.

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Citing papers per year: 2021: 6; 2022: 2; 2023: 9; 2024: 6; 2025: 1; 2026: 2.

26 papers.

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  1. 2022Bayesian Active Learning with Fully Bayesian Gaussian Processescited by 26
  2. 2021Troubleshooting Blind Image Quality Models in the Wildcited by 14
  3. 2022Active Learning for Regression by Inverse Distance Weightingcited by 9
  4. 2021Backdoor Attack and Defense for Deep Regressioncited by 6
  5. 2023An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasetscited by 4
  6. 2024Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognitioncited by 3
  7. 2023Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modelingcited by 2
  8. 2023Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filtercited by 2
  9. 2023On minimizing the training set fill distance in machine learning regressioncited by 2
  10. 2023Streaming Active Learning for Regression Problems Using Regression via Classificationcited by 2
  11. 2021Robust and Active Learning for Deep Neural Network Regressioncited by 1
  12. 2021An Interactive Visualization Tool for Understanding Active Learningcited by 1
14 more
  1. 2023Does Deep Active Learning Work in the Wild?cited by 1
  2. 2024Active learning for regression in engineering populations: A risk-informed approachcited by 1
  3. 2024Active Learning of Deep Neural Networks via Gradient-Free Cutting Planescited by 1
  4. 2024Sample Efficient Robot Learning in Supervised Effect Prediction Taskscited by 1
  5. 2021Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detectioncited by 0
  6. 2021Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learningcited by 0
  7. 2023Through-life Monitoring of Resource-constrained Systems and Fleetscited by 0
  8. 2023Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learningcited by 0
  9. 2023Model Uncertainty based Active Learning on Tabular Data using Boosted Treescited by 0
  10. 2024Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networkscited by 0
  11. 2024Classification Tree-based Active Learning: A Wrapper Approachcited by 0
  12. 2025Regression generation adversarial network based on dual data evaluation strategy for industrial applicationcited by 0
  13. 2026Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimizationcited by 0
  14. 2026Feature Weighting Improves Pool-Based Sequential Active Learning for Regressioncited by 0