Summary of The Real Price Of Bandit Information in Multiclass Classification, by Liad Erez et al.
The Real Price of Bandit Information in Multiclass Classification
by Liad Erez, Alon Cohen, Tomer Koren, Yishay Mansour, Shay Moran
First submitted to arxiv on: 16 May 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 proposed paper revisits the problem of multiclass classification with bandit feedback, where each input is classified into one of K possible labels. The primary focus is on understanding the dependence on the number of labels K and whether T-step regret bounds can be improved beyond the √KT dependence exhibited by existing algorithms. The main contribution is showing that the minimax regret of bandit multiclass has a more nuanced form, including a new algorithm that guarantees regret O(|H|+√T), improving over classical algorithms for moderately-sized hypothesis classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to correctly classify things into one of many categories when you can only get feedback on whether your guess was correct or not. The main idea is to figure out if the number of possible categories affects how well you do, and to come up with a better way of making predictions than what’s already been tried. |
Keywords
» Artificial intelligence » Classification
