Summary of Lil’hdoc: An Algorithm For Good Arm Identification Under Small Threshold Gap, by Tzu-hsien Tsai et al.
lil’HDoC: An Algorithm for Good Arm Identification under Small Threshold Gap
by Tzu-Hsien Tsai, Yun-Da Tsai, Shou-De Lin
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 lil’HDoC algorithm significantly improves the total sample complexity of the HDoC algorithm in solving Good Arm Identification (GAI) problems under a small threshold gap. GAI involves identifying an arm with an expected reward greater than or equal to a given threshold, and lil’HDoC achieves this by bounding the sample complexity of the first output arm while only introducing a negligible term. Experimental results demonstrate that lil’HDoC outperforms state-of-the-art algorithms in both synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new algorithm called lil’HDoC helps find the best option (arm) quickly when we know what a good option looks like. This is important because it can save time and resources. The problem is to figure out which arm has an expected reward as high or higher than a certain goal, even if there’s only a small difference between the rewards. Lil’HDoC works well in both fake and real-world data sets. |
