Summary of Best Arm Identification with Minimal Regret, by Junwen Yang et al.
Best Arm Identification with Minimal Regret
by Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 introduces the problem of best arm identification (BAI) with minimal regret, which combines the goals of regret minimization and BAI. The agent aims to identify the best arm with a prescribed confidence level while minimizing cumulative regret up to the stopping time. The authors establish an instance-dependent lower bound on expected cumulative regret using information-theoretic techniques and present an impossibility result highlighting the tension between cumulative regret and sample complexity in fixed-confidence BAI. They also design and analyze the Double KL-UCB algorithm, which achieves asymptotic optimality as confidence tends to zero. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to solve a problem that comes up when trying to figure out what’s the best option among many possible choices. This problem is important because it helps us understand how we can make good decisions while also minimizing the mistakes we might make. The authors show that there are limits to how well we can do this, and they design a special algorithm that gets close to the best solution. |
