Summary of Revisiting Agnostic Pac Learning, by Steve Hanneke et al.
Revisiting Agnostic PAC Learning
by Steve Hanneke, Kasper Green Larsen, Nikita Zhivotovskiy
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST); 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 explores PAC learning, a classic model for studying supervised learning. In the agnostic setting, researchers have access to a hypothesis set and a training set of labeled samples drawn from an unknown distribution. The goal is to produce a classifier that is competitive with the optimal hypothesis having the least probability of mispredicting labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper studies how to learn from data without knowing the underlying rules or patterns. It’s like trying to develop a smart algorithm that can classify new examples correctly, but you don’t know exactly what makes an example correct or incorrect. The goal is to create a model that performs well, even when faced with unknown situations. |
Keywords
» Artificial intelligence » Probability » Supervised
