Summary of Improving Sample Efficiency Of High Dimensional Bayesian Optimization with Mcmc, by Zeji Yi et al.
Improving sample efficiency of high dimensional Bayesian optimization with MCMC
by Zeji Yi, Yunyue Wei, Chu Xin Cheng, Kaibo He, Yanan Sui
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed method, based on Markov Chain Monte Carlo (MCMC), efficiently samples from an approximated posterior to overcome the curse of dimensionality in high-dimensional spaces. This approach is particularly effective for Gaussian process Thompson sampling, providing theoretical guarantees of convergence. Experimental results demonstrate that both MCMC-based methods outperform state-of-the-art algorithms in sequential optimization and reinforcement learning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method called Markov Chain Monte Carlo to help computers find the best solutions in high-dimensional spaces. This helps solve the “curse of dimensionality” problem, which makes it hard for computers to explore all possible options. The method is based on an idea called Gaussian process Thompson sampling and provides strong guarantees that it will work well. In tests, this new method did better than existing methods in finding good solutions. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
