Summary of Gaussian Process Thompson Sampling Via Rootfinding, by Taiwo A. Adebiyi and Bach Do and Ruda Zhang
Gaussian Process Thompson Sampling via Rootfinding
by Taiwo A. Adebiyi, Bach Do, Ruda Zhang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 This paper presents a novel approach to global optimization for Thompson sampling (TS) with Gaussian process (GP) models. The authors introduce an efficient strategy that selects starting points for gradient-based multi-start optimizers by identifying local optima in the prior sample through univariate rootfinding. This method is used to optimize the posterior sample and improve the overall performance of Bayesian optimization using GP-TS acquisition functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions by finding the best option from a group of possibilities. It does this by using a special kind of math called Gaussian processes, which are great at guessing what might happen in the future. The authors came up with a clever way to use these processes to find the perfect solution. They tested it and found that it works really well, even when there are many options to choose from. |
Keywords
* Artificial intelligence * Optimization
