mlsanityData to 14 Oct 2026

Invariance assumptions for class distribution estimation

arXiv 2311.17225 ↗First posted 28 Nov 2023cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

We study the problem of class distribution estimation under dataset shift. On the training dataset, both features and class labels are observed while on the test dataset only the features can be observed. The task then is the estimation of the distribution of the class labels, i.e. the estimation of the class prior probabilities, in the test dataset. Assumptions of invariance between the training joint distribution of features and labels and the test distribution can considerably facilitate this task. We discuss the assumptions of covariate shift, factorizable joint shift, and sparse joint shift and their implications for class distribution estimation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

11 of 30 references.

References · 11

  1. 2018Marginal Singularity, and the Benefits of Labels in Covariate-Shiftcited by 65
  2. 2018A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptationcited by 23
  3. 2022Estimating and Explaining Model Performance When Both Covariates and Labels Shiftcited by 19
  4. 2021Mapping conditional distributions for domain adaptation under generalized target shiftcited by 18
  5. 2022Understanding new tasks through the lens of training data via exponential tiltingcited by 11
  6. 2021Calibrating sufficientlycited by 10
  7. 2022Class Prior Estimation under Covariate Shift: No Problem?cited by 10
  8. 2022Domain Adaptation with Factorizable Joint Shiftcited by 6
  9. 2023Sparse joint shift in multinomial classificationcited by 5
  10. 2022Factorizable Joint Shift in Multinomial Classificationcited by 4
  11. 2019Temporal Density Extrapolation using a Dynamic Basis Approachcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025On the Interconnections of Calibration, Quantification, and Classifier Accuracy Prediction under Dataset Shiftcited by 2
  2. 2026Multicalibration for Unbiased Model-Based Prevalence Estimationcited by 0
  3. 2026Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shiftcited by 0
  4. 2026Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independencecited by 0