Summary of Asymptotically Optimal Fixed-budget Best Arm Identification with Variance-dependent Bounds, by Masahiro Kato et al.
Asymptotically Optimal Fixed-Budget Best Arm Identification with Variance-Dependent Bounds
by Masahiro Kato, Masaaki Imaizumi, Takuya Ishihara, Toru Kitagawa
First submitted to arxiv on: 6 Feb 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Econometrics (econ.EM); Statistics Theory (math.ST); Methodology (stat.ME); 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 paper investigates fixed-budget best arm identification (BAI) to minimize expected simple regret in adaptive experiments. It proposes the Two-Stage (TS)-Hirano-Imbens-Ridder (HIR) strategy, which is asymptotically minimax optimal and matches the worst-case lower bound for expected simple regret. The method uses the HIR estimator (Hirano et al., 2003) to recommend the best arm, considering the variances of potential outcomes. Theoretical analysis shows its effectiveness in minimizing regret, while simulations validate its performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem where you have to choose the best option from many possibilities, but you only get feedback after making a choice. It wants to find the best way to make this choice so that it’s as good as possible, even if you don’t know everything ahead of time. The researchers propose a new method that is really good at finding the best option and test it with simulations. |
