# A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation

arXiv: https://arxiv.org/abs/1810.01545 · HTML: https://mlsanity.com/paper/1810.01545/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 23 of 23)

- [Calibrating sufficiently](https://mlsanity.com/paper/2105.07283/) (2021)
- [Class Prior Estimation under Covariate Shift: No Problem?](https://mlsanity.com/paper/2206.02449/) (2022)
- [Mixture Proportion Estimation Beyond Irreducibility](https://mlsanity.com/paper/2306.01253/) (2023)
- [Label Noise: Ignorance Is Bliss](https://mlsanity.com/paper/2411.00079/) (2024)
- [Neyman-Pearson Multi-class Classification via Cost-sensitive Learning](https://mlsanity.com/paper/2111.04597/) (2021)
- [Sparse joint shift in multinomial classification](https://mlsanity.com/paper/2303.16971/) (2023)
- [Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift](https://mlsanity.com/paper/2306.16406/) (2023)
- [Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints](https://mlsanity.com/paper/2406.20088/) (2024)
- [Adaptive transfer learning](https://mlsanity.com/paper/2106.04455/) (2021)
- [Factorizable Joint Shift in Multinomial Classification](https://mlsanity.com/paper/2207.14514/) (2022)
- [Invariance assumptions for class distribution estimation](https://mlsanity.com/paper/2311.17225/) (2023)
- [Help Me Help You: The Aggregate Value of Source and Target Data in Transfer Learning](https://mlsanity.com/paper/2408.16189/) (2024)
- [Classification Tree Pruning Under Covariate Shift](https://mlsanity.com/paper/2305.04335/) (2023)
- [Tight Rates in Supervised Outlier Transfer Learning](https://mlsanity.com/paper/2310.04686/) (2023)
- [Distribution-Free Rates in Neyman-Pearson Classification](https://mlsanity.com/paper/2402.09560/) (2024)
- [Conformal Prediction Under Generalized Covariate Shift with Posterior Drift](https://mlsanity.com/paper/2502.17744/) (2025)
- [Domain Generalization Under Posterior Drift](https://mlsanity.com/paper/2510.04441/) (2025)
- [Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer](https://mlsanity.com/paper/2501.10870/) (2025)
- [Recalibrating binary probabilistic classifiers](https://mlsanity.com/paper/2505.19068/) (2025)
- [An adaptive transfer learning perspective on classification in non-stationary environments](https://mlsanity.com/paper/2405.18091/) (2024)
- [DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation](https://mlsanity.com/paper/2408.02045/) (2024)
- [Factorizable joint shift revisited](https://mlsanity.com/paper/2601.15036/) (2026)
- [Retrieval-Based In-Context Learning: A Domain Adaptation Framework](https://mlsanity.com/paper/2610.05717/) (2026)

## Abstract (arXiv)

In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source. Previous work on this problem has assumed that the performance measure of interest is the expected value of some loss function. We introduce a new Neyman-Pearson-like criterion and argue that, for this optimality criterion, stronger domain adaptation results are possible than what has previously been established. In particular, we study a class of domain adaptation problems that generalizes both the covariate shift assumption and a model for feature-dependent label noise, and establish optimal classification on the target domain despite not having access to labelled data from this domain.