Summary of Is Transductive Learning Equivalent to Pac Learning?, by Shaddin Dughmi et al.
Is Transductive Learning Equivalent to PAC Learning?
by Shaddin Dughmi, Yusuf Kalayci, Grayson York
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Statistics Theory (math.ST)
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 abstract discusses the relationship between probably approximately correct (PAC) learners and transductive models of learning in the context of realizable and agnostic learning. Modest extensions of existing results show that these models are essentially equivalent for realizable learning, with modest differences in sample complexity for agnostic learning. The paper’s main contributions explore the exact relationship between these two models, providing insight into their performance and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how two types of machine learning models, PAC learners and transductive models, work together to learn from data. It shows that they’re very similar when it comes to realizable learning, which means we can use one model as a starting point for the other. However, there are some differences in how well they perform when trying to make predictions without seeing all the training data. The main thing this paper does is figure out exactly how these models work together and what their strengths and weaknesses are. |
Keywords
» Artificial intelligence » Machine learning
