Summary of Robust Entropy Search For Safe Efficient Bayesian Optimization, by Dorina Weichert et al.
Robust Entropy Search for Safe Efficient Bayesian Optimization
by Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link, Gunar Ernis
First submitted to arxiv on: 29 May 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 The proposed Robust Entropy Search (RES) acquisition function addresses the challenges of Bayesian Optimization (BO) in engineering applications by efficiently sampling controllable parameters while also finding a robust solution. In the context of adversarial robustness, where some parameters are uncontrollable or perturbed at application time, RES outperforms state-of-the-art algorithms in experiments on synthetic and real-life data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to use Bayesian Optimization to find good solutions that remain good even when some things change. The method is called Robust Entropy Search. It helps by choosing the right parameters to try next, taking into account what might happen later. The results are impressive – it works well on both made-up and real data. |
Keywords
» Artificial intelligence » Optimization
