Summary of Fast, Precise Thompson Sampling For Bayesian Optimization, by David Sweet
Fast, Precise Thompson Sampling for Bayesian Optimization
by David Sweet
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper presents an improved version of Thompson sampling, called Stagger Thompson Sampler (STS), which outperforms other acquisition methods in Bayesian optimization. By more precisely locating the maximizer using less computation time, STS achieves better results than previous methods like TS and P-Star Sampler (PSS). The authors demonstrate the effectiveness of STS through numerical experiments on test functions across various dimensions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a technique called Thompson sampling to find the best option. They make it work faster and more accurately by using less computer power. This helps with finding the best solution in problems where you need to try different options. The results show that this new method, Stagger Thompson Sampler (STS), works better than other methods. |
Keywords
* Artificial intelligence * Optimization
