Summary of Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds, by Shion Takeno et al.
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
by Shion Takeno, Yu Inatsu, Masayuki Karasuyama
First submitted to arxiv on: 3 Feb 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 new approach to black-box optimization called Improved Randomized Gaussian Process Upper Confidence Bound (IRGP-UCB). It builds upon earlier work that introduced Randomized GP-UCB (RGP-UCB), which uses a randomized confidence parameter to mitigate the impact of manually specifying a value for β. The authors generalize the regret analysis of RGP-UCB to a wider class of distributions, including the Gamma distribution, and propose IRGP-UCB based on a two-parameter exponential distribution. This new approach achieves tighter Bayesian regret bounds and avoids over-exploration in later iterations by not requiring an increase in the confidence parameter with the number of iterations. The authors demonstrate the effectiveness of IRGP-UCB through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IRGP-UCB is a way to optimize things when you don’t know how they work. It’s like trying different paths to find the best one without knowing the map. The problem was that earlier versions needed to choose a value for β, which wasn’t very good because it could lead to mistakes. So, this new approach uses random values for β to make it better. It works by looking at what went well in the past and using that to decide what to try next. This helps avoid trying things that didn’t work before. The authors tested IRGP-UCB many times and showed that it really does work well. |
Keywords
* Artificial intelligence * Optimization
