Summary of On Improved Regret Bounds in Bayesian Optimization with Gaussian Noise, by Jingyi Wang et al.
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise
by Jingyi Wang, Haowei Wang, Cosmin G. Petra, Nai-Yuan Chiang
First submitted to arxiv on: 25 Dec 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 Bayesian optimization with Gaussian process surrogate models is a powerful black-box optimization method. The paper focuses on the convergence analysis of Bayesian optimization algorithms under frequentist settings for the objective. Specifically, it establishes new pointwise bounds on the prediction error of Gaussian processes under Gaussian noise, which improves the convergence rates of cumulative regret bound for GP-UCB and GP-TS. Additionally, the results can be applied to general BO algorithms and convergence analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a special kind of computer program to find the best way to do something without knowing how it works. The program uses information from the past to make good choices for the future. It’s like a game where you try different things to see what works best. The researchers in this paper are trying to figure out how well this program does as it tries to find the best solution. They’re using special math tools to help them understand how the program makes decisions and how well it does over time. |
Keywords
* Artificial intelligence * Optimization
