# Invariance assumptions for class distribution estimation

arXiv: https://arxiv.org/abs/2311.17225 · HTML: https://mlsanity.com/paper/2311.17225/

## 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 (most cited first, 11 of 11)

- [Marginal Singularity, and the Benefits of Labels in Covariate-Shift](https://mlsanity.com/paper/1803.01833/) (2018)
- [A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation](https://mlsanity.com/paper/1810.01545/) (2018)
- [Estimating and Explaining Model Performance When Both Covariates and Labels Shift](https://mlsanity.com/paper/2209.08436/) (2022)
- [Mapping conditional distributions for domain adaptation under generalized target shift](https://mlsanity.com/paper/2110.15057/) (2021)
- [Understanding new tasks through the lens of training data via exponential tilting](https://mlsanity.com/paper/2205.13577/) (2022)
- [Calibrating sufficiently](https://mlsanity.com/paper/2105.07283/) (2021)
- [Class Prior Estimation under Covariate Shift: No Problem?](https://mlsanity.com/paper/2206.02449/) (2022)
- [Domain Adaptation with Factorizable Joint Shift](https://mlsanity.com/paper/2203.02902/) (2022)
- [Sparse joint shift in multinomial classification](https://mlsanity.com/paper/2303.16971/) (2023)
- [Factorizable Joint Shift in Multinomial Classification](https://mlsanity.com/paper/2207.14514/) (2022)
- [Temporal Density Extrapolation using a Dynamic Basis Approach](https://mlsanity.com/paper/1906.00912/) (2019)

## Built on by (most cited first, 4 of 4)

- [On the Interconnections of Calibration, Quantification, and Classifier Accuracy Prediction under Dataset Shift](https://mlsanity.com/paper/2505.11380/) (2025)
- [Multicalibration for Unbiased Model-Based Prevalence Estimation](https://mlsanity.com/paper/2604.21549/) (2026)
- [Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift](https://mlsanity.com/paper/2605.31250/) (2026)
- [Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence](https://mlsanity.com/paper/2606.07914/) (2026)

## 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.