mlsanityData to 7 Oct 2026

Active Learning under Label Shift

arXiv 2007.08479 ↗First posted 16 Jul 2020cs.LG stat.MLcited by 23

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Abstract (arXiv)

We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling and propose their combined usage in active learning. Our method is known as Mediated Active Learning under Label Shift (MALLS). It balances the bias from class-balanced sampling and the variance from importance weighting. We prove sample complexity and generalization guarantees for MALLS which show active learning reduces asymptotic sample complexity even under arbitrary label shift. We empirically demonstrate MALLS scales to high-dimensional datasets and can reduce the sample complexity of active learning by 60% in deep active learning tasks.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2020.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 2; 2024: 5; 2025: 4; 2026: 2.

23 papers.

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  1. 2024A Survey on Deep Active Learning: Recent Advances and New Frontierscited by 53
  2. 2022Estimating and Explaining Model Performance When Both Covariates and Labels Shiftcited by 19
  3. 2022Optimizing Data Collection for Machine Learningcited by 15
  4. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  5. 2021Did the Model Change? Efficiently Assessing Machine Learning API Shiftscited by 13
  6. 2022Active Learning Over Multiple Domains in Natural Language Taskscited by 11
  7. 2021Bayesian Active Learning for Sim-to-Real Robotic Perceptioncited by 8
  8. 2022Active Learning with Expected Error Reductioncited by 7
  9. 2022Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructionscited by 7
  10. 2024On the Pros and Cons of Active Learning for Moral Preference Elicitationcited by 6
  11. 2021Corruption Robust Active Learningcited by 3
  12. 2022Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarioscited by 3
11 more
  1. 2023ASPEST: Bridging the Gap Between Active Learning and Selective Predictioncited by 3
  2. 2025Distributionally Robust Active Learning for Gaussian Process Regressioncited by 3
  3. 2023Generator Assisted Mixture of Experts For Feature Acquisition in Batchcited by 2
  4. 2024LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Trainingcited by 2
  5. 2025Open Set Label Shift with Test Time Out-of-Distribution Referencecited by 1
  6. 2025Bayesian-based Online Label Shift Estimation with Dynamic Dirichlet Priorscited by 1
  7. 2026Discovery of Feasible 3D Printing Configurations for Metal Alloys via AI-driven Adaptive Experimental Designcited by 1
  8. 2022High Dimensional Binary Classification under Label Shift: Phase Transition and Regularizationcited by 0
  9. 2024Theory-inspired Label Shift Adaptation via Aligned Distribution Mixturecited by 0
  10. 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancementcited by 0
  11. 2026When Imbalance Comes Twice: Active Learning under Simulated Class Imbalance and Label Shift in Binary Semantic Segmentationcited by 0