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Factorizable joint shift revisited

arXiv 2601.15036 ↗First posted 21 Jan 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

Factorizable joint shift (FJS) represents a type of distribution shift (or dataset shift) that comprises both covariate and label shift. Recently, it has been observed that FJS actually arises from consecutive label and covariate (or vice versa) shifts. Research into FJS so far has been confined mostly to the case of categorical labels. We propose a framework for analysing distribution shift in the case of a general label space, thus covering both classification and regression models. Based on the framework, we generalise existing results on FJS to general label spaces and present and analyse a related extension to label distribution estimation of the expectation maximisation (EM) algorithm for class prior probabilities. We also take a fresh look at generalized label shift (GLS) in the case of a general label space.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 54 references that are in the map. First posted on arXiv 21 Jan 2026.

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References · 16

  1. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 364
  2. 2019A review of domain adaptation without target labelscited by 154
  3. 2020Domain Adaptation with Conditional Distribution Matching and Generalized Label Shiftcited by 124
  4. 2019Support and Invertibility in Domain-Invariant Representationscited by 87
  5. 2018A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptationcited by 22
  6. 2017Fisher consistency for prior probability shiftcited by 20
  7. 2022Estimating and Explaining Model Performance When Both Covariates and Labels Shiftcited by 19
  8. 2020Importance Weight Estimation and Generalization in Domain Adaptation under Label Shiftcited by 10
  9. 2021Calibrating sufficientlycited by 10
  10. 2022Domain Adaptation with Factorizable Joint Shiftcited by 6
  11. 2023Sparse joint shift in multinomial classificationcited by 5
  12. 2023Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithmscited by 5
4 more
  1. 2022Factorizable Joint Shift in Multinomial Classificationcited by 4
  2. 2014Exact fit of simple finite mixture modelscited by 3
  3. 2025Kernel Density Machinescited by 2
  4. 2025Recalibrating binary probabilistic classifierscited by 1

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