Summary of Generalized Neyman Allocation For Locally Minimax Optimal Best-arm Identification, by Masahiro Kato
Generalized Neyman Allocation for Locally Minimax Optimal Best-Arm Identification
by Masahiro Kato
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Econometrics (econ.EM); 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 A machine learning algorithm for identifying the best arm in a multi-armed bandit problem is proposed. The Generalized Neyman Allocation (GNA) algorithm provides an asymptotically minimax optimal solution for fixed-budget best-arm identification, with tight bounds on the probability of misidentifying the best arm. This improves upon existing algorithms and addresses a longstanding open issue in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates an algorithm to help identify which option is best out of many choices. It’s like trying to figure out which ice cream flavor people will like most. The new algorithm, called GNA, makes sure it finds the best choice within a certain range and doesn’t get it wrong very often. |
Keywords
» Artificial intelligence » Machine learning » Probability
