Summary of Fixed Confidence Best Arm Identification in the Bayesian Setting, by Kyoungseok Jang et al.
Fixed Confidence Best Arm Identification in the Bayesian Setting
by Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki
First submitted to arxiv on: 16 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 In this paper, researchers tackle the problem of fixed-confidence best arm identification (FC-BAI) in the Bayesian setting. The goal is to find the arm with the largest mean reward with a fixed confidence level, given that the bandit model has been sampled from a known prior distribution. Unlike previous studies, which focused on the frequentist approach, this work explores the FC-BAI problem in the Bayesian framework. The authors show that traditional algorithms like track-and-stop and top-two are suboptimal in the Bayesian setting and derive a lower bound for the expected number of samples required to achieve optimal performance. They also introduce a variant of successive elimination algorithm that achieves near-optimal performance, up to a logarithmic factor. Simulation results verify the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to get a reward in a game-like situation called bandit model. The researchers wanted to see how well some old methods work when they’re used in a special kind of math problem that involves uncertainty. They found out that these old methods aren’t very good at this new type of problem, and then they came up with a better way to solve it using something called successive elimination. This new method is really close to being the best, but not quite. The authors tested their ideas on some fake data and got results that match what they expected. |
