Summary of Regret Analysis For Randomized Gaussian Process Upper Confidence Bound, by Shion Takeno et al.
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
by Shion Takeno, Yu Inatsu, Masayuki Karasuyama
First submitted to arxiv on: 2 Sep 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 Gaussian process upper confidence bound (GP-UCB) is an established algorithm for Bayesian optimization, assuming the objective function follows a Gaussian process. However, GP-UCB’s theoretical confidence parameter increases along with iterations, which is a notable drawback. This paper addresses this issue by analyzing the improved randomized GP-UCB (IRGP-UCB), which generates confidence parameters from a shifted exponential distribution. IRGP-UCB achieves sub-linear regret upper bounds without increasing the confidence parameter when the input domain is finite. Numerical experiments using synthetic, benchmark functions, and real-world emulators demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper fixes a problem with an algorithm for finding the best combination of things to do something well. The old algorithm got better at predicting how good it was going to be, but only because it became less careful about trying new things. The new version keeps being careful and still gets better results in the end. |
Keywords
» Artificial intelligence » Objective function » Optimization
