Summary of Near-optimal Algorithm For Non-stationary Kernelized Bandits, by Shogo Iwazaki and Shion Takeno
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
by Shogo Iwazaki, Shion Takeno
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The paper presents a novel approach to minimizing regret in time-varying Bayesian optimization, also known as non-stationary kernelized bandit (KB) problems. The authors focus on developing a near-optimal algorithm that matches the regret upper and lower bounds, while overcoming feasibility issues caused by high computational costs. To achieve this, they propose a new algorithm called restarting phased elimination with random permutation (R-PERP), which leverages simple permutation procedures to derive a tighter confidence bound tailored to non-stationary problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the best way to make decisions when the reward function changes over time. The authors want to find an algorithm that doesn’t require too much computation and can keep up with these changing rewards. They come up with a new method called R-PERP, which helps them achieve this goal. |
Keywords
* Artificial intelligence * Optimization
