Summary of Transductive Learning Is Compact, by Julian Asilis et al.
Transductive Learning Is Compact
by Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); Logic in Computer Science (cs.LO); 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 This paper demonstrates a compactness result that applies to various supervised learning scenarios with different types of loss functions. Specifically, it shows that any hypothesis class H can be learned when its finite projections are learnable. The study proves that this exact form of compactness holds for realizable and agnostic learning with proper metric losses and continuous losses on compact spaces. For realizable learning with improper metric losses, the paper finds a gap of a factor of 2 between sample complexities. It also explores the implications of these findings in the PAC model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that some things are possible when we learn new information from labeled examples, and it helps us understand how much data we need to make good predictions. The researchers found out that if we can predict well using smaller sets of labeled data, then we can also do well with larger sets of data. This is important because it means we don’t need as many labeled examples to learn new things. |
Keywords
* Artificial intelligence * Supervised
