# PAC-Bayes and Domain Adaptation

arXiv: https://arxiv.org/abs/1707.05712 · HTML: https://mlsanity.com/paper/1707.05712/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 16 of 16)

- [Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve](https://mlsanity.com/paper/2102.03924/) (2021)
- [A General Framework for the Practical Disintegration of PAC-Bayesian Bounds](https://mlsanity.com/paper/2102.08649/) (2021)
- [Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory](https://mlsanity.com/paper/2310.04935/) (2023)
- [Information-Theoretic Analysis of Unsupervised Domain Adaptation](https://mlsanity.com/paper/2210.00706/) (2022)
- [PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners](https://mlsanity.com/paper/2207.05685/) (2022)
- [PAC-Bayesian Lifelong Learning For Multi-Armed Bandits](https://mlsanity.com/paper/2203.03303/) (2022)
- [LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue](https://mlsanity.com/paper/2210.07777/) (2022)
- [Unsupervised domain adaptation by learning using privileged information](https://mlsanity.com/paper/2303.09350/) (2023)
- [Out-Of-Domain Unlabeled Data Improves Generalization](https://mlsanity.com/paper/2310.00027/) (2023)
- [Bayesian Transfer Learning](https://mlsanity.com/paper/2312.13484/) (2023)
- [Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis](https://mlsanity.com/paper/2405.09516/) (2024)
- [Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights](https://mlsanity.com/paper/2207.07255/) (2022)
- [Learning from few examples: Classifying sex from retinal images via deep learning](https://mlsanity.com/paper/2207.09624/) (2022)
- [Practicality of generalization guarantees for unsupervised domain adaptation with neural networks](https://mlsanity.com/paper/2303.08720/) (2023)
- [Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models](https://mlsanity.com/paper/2603.23783/) (2026)
- [Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift](https://mlsanity.com/paper/2609.31960/) (2026)

## Abstract (arXiv)

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence-expressed as a ratio-controls the trade-off between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.