Summary of Optimal Thresholding Linear Bandit, by Eduardo Ochoa Rivera and Ambuj Tewari
Optimal Thresholding Linear Bandit
by Eduardo Ochoa Rivera, Ambuj Tewari
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 a new type of pure exploration problem, known as the -Thresholding Bandit Problem (TBP), which involves stochastic linear bandits with fixed confidence. The authors prove a lower bound for the sample complexity and adapt an existing algorithm designed for Best Arm Identification in the linear case to TBP, achieving asymptotic optimality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how well we can learn about unknown rewards when we only have limited data. It tackles this challenge by designing a new algorithm that works well even when the environment is complex and noisy. The researchers show that their approach can efficiently explore the reward landscape to find the best option, which has important implications for real-world applications. |
