# PAC-Bayesian Domain Adaptation Bounds for Multi-view learning

arXiv: https://arxiv.org/abs/2401.01048 · HTML: https://mlsanity.com/paper/2401.01048/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [User-friendly introduction to PAC-Bayes bounds](https://mlsanity.com/paper/2110.11216/) (2021)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [A Primer on PAC-Bayesian Learning](https://mlsanity.com/paper/1901.05353/) (2019)
- [Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm](https://mlsanity.com/paper/1503.08329/) (2015)
- [A New PAC-Bayesian Perspective on Domain Adaptation](https://mlsanity.com/paper/1506.04573/) (2015)
- [PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners](https://mlsanity.com/paper/2207.05685/) (2022)
- [Domain Adaptation in Multi-View Embedding for Cross-Modal Video Retrieval](https://mlsanity.com/paper/2110.12812/) (2021)
- [PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers](https://mlsanity.com/paper/1503.06944/) (2015)
- [PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach](https://mlsanity.com/paper/1606.07240/) (2016)
- [PAC-Bayes Analysis of Multi-view Learning](https://mlsanity.com/paper/1406.5614/) (2014)

## Built on by (most cited first, 2 of 2)

- [Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models](https://mlsanity.com/paper/2603.23783/) (2026)
- [Position Paper: From Edge AI to Adaptive Edge AI](https://mlsanity.com/paper/2604.07360/) (2026)

## Abstract (arXiv)

This paper presents a series of new results for domain adaptation in the multi-view learning setting. The incorporation of multiple views in the domain adaptation was paid little attention in the previous studies. In this way, we propose an analysis of generalization bounds with Pac-Bayesian theory to consolidate the two paradigms, which are currently treated separately. Firstly, building on previous work by Germain et al., we adapt the distance between distribution proposed by Germain et al. for domain adaptation with the concept of multi-view learning. Thus, we introduce a novel distance that is tailored for the multi-view domain adaptation setting. Then, we give Pac-Bayesian bounds for estimating the introduced divergence. Finally, we compare the different new bounds with the previous studies.