Summary of Boundary Exploration For Bayesian Optimization with Unknown Physical Constraints, by Yunsheng Tian et al.
Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
by Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt, Payel Das, Jie Chen, Wojciech Matusik, Mina Konaković Luković
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 Bayesian optimization method, BE-CBO, addresses the challenge of optimizing an unknown function with unknown constraints in real-world applications. By efficiently exploring the boundary between feasible and infeasible designs, BE-CBO outperforms state-of-the-art methods on synthetic and real-world benchmarks. The approach learns constraints using an ensemble of neural networks that outperform Gaussian Processes for capturing complex boundaries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to optimize things when we don’t know what’s possible or impossible. It’s like trying to find the edge between what can be done and what can’t. The method, called BE-CBO, uses special kinds of networks to figure out where those edges are. This helps it make better choices than other methods. It works well on fake data and real-world problems. |
Keywords
* Artificial intelligence * Optimization
