mlsanityData to 7 Oct 2026

Active Learning for Cost-Sensitive Classification

arXiv 1703.01014 ↗First posted 3 Mar 2017cs.LG stat.MLcited by 40

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Abstract (arXiv)

We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that could be the best, ignoring the sure losers. We prove COAL can be efficiently implemented for any regression family that admits squared loss optimization; it also enjoys strong guarantees with respect to predictive performance and labeling effort. We empirically compare COAL to passive learning and several active learning baselines, showing significant improvements in labeling effort and test cost on real-world datasets.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 10; 2023: 6; 2024: 2; 2025: 8; 2026: 3.

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  1. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 72
  2. 2021Smoothed Analysis with Adaptive Adversariescited by 57
  3. 2021Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discriminationcited by 53
  4. 2022Sample Efficient Learning of Predictors that Complement Humanscited by 46
  5. 2022Making Decisions under Outcome Performativitycited by 32
  6. 2022Efficient Active Learning with Abstentioncited by 21
  7. 2023Streaming Active Learning with Deep Neural Networkscited by 20
  8. 2022Oracle-Efficient Online Learning for Beyond Worst-Case Adversariescited by 19
  9. 2021Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noisecited by 18
  10. 2021A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classificationcited by 17
  11. 2022Instance-optimal PAC Algorithms for Contextual Banditscited by 16
  12. 2023Adapting to Continuous Covariate Shift via Online Density Ratio Estimationcited by 15
28 more
  1. 2021Active Learning in Bayesian Neural Networks with Balanced Entropy Learning Principlecited by 14
  2. 2022Active Learning with Neural Networks: Insights from Nonparametric Statisticscited by 13
  3. 2021Bayesian Active Learning by Disagreements: A Geometric Perspectivecited by 9
  4. 2021Going Beyond Linear RL: Sample Efficient Neural Function Approximationcited by 9
  5. 2023Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learningcited by 9
  6. 2022Promises and Pitfalls of Threshold-based Auto-labelingcited by 6
  7. 2025Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Modelscited by 6
  8. 2021Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspectivecited by 4
  9. 2023Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximationcited by 4
  10. 2024Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learningcited by 4
  11. 2025Revisiting Active Learning under (Human) Label Variationcited by 4
  12. 2026Active Testing of Large Language Models via Approximate Neyman Allocationcited by 4
  13. 2021Corruption Robust Active Learningcited by 3
  14. 2024Robust Offline Policy Learning with Observational Data from Multiple Sourcescited by 3
  15. 2025Enhancing Cost Efficiency in Active Learning with Candidate Set Querycited by 3
  16. 2025Towards Multimodal Active Learning: Efficient Learning with Limited Paired Datacited by 3
  17. 2022Efficient and Reliable Probabilistic Interactive Learning with Structured Outputscited by 2
  18. 2022Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detectioncited by 2
  19. 2025On The Statistical Complexity of Offline Decision-Makingcited by 2
  20. 2021Online Sub-Sampling for Reinforcement Learning with General Function Approximationcited by 1
  21. 2022Black-box Generalization of Machine Teachingcited by 1
  22. 2025Towards Fundamental Limits for Active Multi-distribution Learningcited by 1
  23. 2026Loss-Driven Bayesian Active Learningcited by 1
  24. 2023Leveraging User-Triggered Supervision in Contextual Banditscited by 0
  25. 2023Nonparametric active learning for cost-sensitive classificationcited by 0
  26. 2025Active Learning via Regression Beyond Realizabilitycited by 0
  27. 2025ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraintscited by 0
  28. 2026Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalancecited by 0