Summary of Replicability Is Asymptotically Free in Multi-armed Bandits, by Junpei Komiyama et al.
Replicability is Asymptotically Free in Multi-armed Bandits
by Junpei Komiyama, Shinji Ito, Yuichi Yoshida, Souta Koshino
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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 is designed to ensure that its sequence of actions is not affected by randomness in the dataset. This “replicability” allows third parties to reproduce published findings and helps researchers apply standard statistical tests. The existing algorithms require more regret (a measure of suboptimality) compared to non-replicable algorithms, but this additional cost can be avoided when the time horizon is sufficiently large. The proposed algorithm only suffers a smaller amount of exploration when the time horizon is large. To ensure replicability, randomness is incorporated into the decision-making process. A principled approach is proposed to limit the probability of nonreplication. The paper also derives a lower bound for the two-armed replicable bandit problem, implying the optimality of the proposed algorithm up to a log-log T factor. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about an algorithm that makes sure its actions aren’t affected by randomness in the data. This is important because it lets other people reproduce the results and helps researchers check their findings. The algorithm is better than existing ones when you have enough time, but it only explores less than existing algorithms do. To make this work, the algorithm uses randomness to decide what to do next. It also finds a new way to limit how likely it is that someone else can’t reproduce the results. This helps us understand why some research has worked in the past. |
Keywords
* Artificial intelligence * Machine learning * Probability
