Summary of A Characterization Of List Regression, by Chirag Pabbaraju et al.
A Characterization of List Regression
by Chirag Pabbaraju, Sahasrajit Sarmasarkar
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); 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 sample complexity of list learning tasks, which involve generating a short list of predictions where at least one prediction is correct. The authors focus on understanding the characteristics of this type of task, building upon recent works that have explored the PAC (probably approximately correct) sample complexity of standard and online list classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to make accurate lists of predictions, where we only need one prediction to be right. It’s like a game show where you try to guess multiple answers, but you just need one to be correct. The researchers are trying to figure out how many examples (or samples) you need to learn this skill. |
Keywords
* Artificial intelligence * Classification
