Summary of Pac-bayesian Domain Adaptation Bounds For Multi-view Learning, by Mehdi Hennequin and Khalid Benabdeslem and Haytham Elghazel
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning
by Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework consolidates domain adaptation and multi-view learning by introducing a novel distance metric tailored for the multi-view domain adaptation setting. Building on previous work, the authors adapt the distance between distributions for domain adaptation in the presence of multiple views. The framework is then analyzed using Pac-Bayesian theory to derive generalization bounds for estimating the introduced divergence. The results are compared to previous studies, providing a comprehensive understanding of the intersection of domain adaptation and multi-view learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper brings together two important ideas in machine learning: domain adaptation and multi-view learning. Usually, these topics are studied separately, but this research shows how they can be connected to help machines learn more easily when moving between different situations or environments with different types of data. The authors developed a new way to measure the distance between different groups of information, which helps them figure out how well a machine will do in a new situation. They also used a mathematical tool called Pac-Bayesian theory to understand how accurate their measurements are. Finally, they compared their results to what other researchers have found. |
Keywords
* Artificial intelligence * Domain adaptation * Generalization * Machine learning
