Summary of Multi-view Majority Vote Learning Algorithms: Direct Minimization Of Pac-bayesian Bounds, by Mehdi Hennequin et al.
Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
by Mehdi Hennequin, Abdelkrim Zitouni, Khalid Benabdeslem, Haytham Elghazel, Yacine Gaci
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research extends the PAC-Bayesian framework to multi-view learning, a setting where multiple complementary data representations are used. Novel generalization bounds based on Rényi divergence are introduced, offering an alternative to traditional Kullback-Leibler divergence-based approaches. The study also proposes first- and second-order oracle PAC-Bayesian bounds and extends the C-bound to multi-view settings. To bridge theory and practice, efficient self-bounding optimization algorithms are designed that align with theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes a famous statistical learning approach called PAC-Bayesian and applies it to multiple types of data. This helps us understand how well we can make predictions when we have many different ways of looking at the same problem. The researchers come up with new rules for predicting accuracy, which are based on something called Rényi divergence. They also create algorithms that work together seamlessly. |
Keywords
» Artificial intelligence » Generalization » Optimization
