Summary of Online Learning with Sublinear Best-action Queries, by Matteo Russo et al.
Online Learning with Sublinear Best-Action Queries
by Matteo Russo, Andrea Celli, Riccardo Colini Baldeschi, Federico Fusco, Daniel Haimovich, Dima Karamshuk, Stefano Leonardi, Niek Tax
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 reexamines online learning by allowing a decision maker to acquire additional information about the actions they will select. This is done through “best-action queries” that reveal which action is best at each time step. The researchers study how well algorithms can perform when given access to these queries, with a focus on feedback models and the number of queries available (up to k). They establish tight bounds on performance for different types of feedback models, showing significant advantages in regret rates even with a modest number of queries. Additionally, they analyze the challenging setting where feedback is only obtained during the time steps corresponding to the best-action queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make better decisions online by giving you more information before making a choice. Imagine if you could know which option will give you the best outcome ahead of time. The researchers study how well algorithms can do when they’re given this extra information, and how many times you can ask for it (up to k). They show that even with just a little extra information, you can make much better choices than without it. |
Keywords
* Artificial intelligence * Online learning
