mlsanityData to 7 Oct 2026

Minimax optimal approaches to the label shift problem in non-parametric settings

arXiv 2003.10443 ↗First posted 23 Mar 2020math.ST stat.ML stat.THcited by 20

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Abstract (arXiv)

We study the minimax rates of the label shift problem in non-parametric classification. In addition to the unsupervised setting in which the learner only has access to unlabeled examples from the target domain, we also consider the setting in which a small number of labeled examples from the target domain is available to the learner. Our study reveals a difference in the difficulty of the label shift problem in the two settings, and we attribute this difference to the availability of data from the target domain to estimate the class conditional distributions in the latter setting. We also show that a class proportion estimation approach is minimax rate-optimal in the unsupervised setting.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Mar 2020.

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Citing papers per year: 2021: 1; 2022: 6; 2023: 3; 2024: 4; 2025: 6.

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  1. 2022Optimally tackling covariate shift in RKHS-based nonparametric regressioncited by 53
  2. 2025CARROT: A Cost Aware Rate Optimal Routercited by 34
  3. 2022Transfer Learning for Contextual Multi-armed Banditscited by 24
  4. 2023Pseudo-Labeling for Kernel Ridge Regression under Covariate Shiftcited by 17
  5. 2024A transfer learning framework for weak-to-strong generalizationcited by 14
  6. 2022Understanding new tasks through the lens of training data via exponential tiltingcited by 11
  7. 2023ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Modelscited by 9
  8. 2025Provable Benefits of Unsupervised Pre-training and Transfer Learning via Single-Index Modelscited by 6
  9. 2021Adaptive transfer learningcited by 4
  10. 2022On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Modelscited by 4
  11. 2024Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraintscited by 4
  12. 2022Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classificationcited by 3
8 more
  1. 2025Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfercited by 2
  2. 2022Importance Weighting Correction of Regularized Least-Squares for Target Shiftcited by 1
  3. 2025Open Set Label Shift with Test Time Out-of-Distribution Referencecited by 1
  4. 2025Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameterscited by 1
  5. 2025Limitations of refinement methods for weak to strong generalizationcited by 1
  6. 2023Addressing Distribution Shift in RTB Markets via Exponential Tiltingcited by 0
  7. 2024An adaptive transfer learning perspective on classification in non-stationary environmentscited by 0
  8. 2024Theory-inspired Label Shift Adaptation via Aligned Distribution Mixturecited by 0