Summary of Is Efficient Pac Learning Possible with An Oracle That Responds ‘yes’ or ‘no’?, by Constantinos Daskalakis and Noah Golowich
Is Efficient PAC Learning Possible with an Oracle That Responds ‘Yes’ or ‘No’?
by Constantinos Daskalakis, Noah Golowich
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper investigates the empirical risk minimization (ERM) principle in machine learning, exploring whether ERM is necessary for efficient learning. It answers affirmatively, showing that a weaker oracle can be used to achieve learnability in binary classification, with polynomial sample and oracle complexity depending on the VC dimension of the hypothesis class. The results extend to agnostic learning, partial concept classes, multiclass, and real-valued settings, providing algorithmic principles for efficient learnability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning works best when it tries to find a good answer by looking at what happened before. It wants to know if this way of working is the only one that makes sense. The researchers found that there’s another way to do it, and it still works really well! This new way uses less information than the first method, but it’s still very accurate. They also tested this new way on different types of problems and found that it worked just as well. |
Keywords
» Artificial intelligence » Classification » Machine learning
