Summary of Batched Bayesian Optimization with Correlated Candidate Uncertainties, by Jenna Fromer et al.
Batched Bayesian optimization with correlated candidate uncertainties
by Jenna Fromer, Runzhong Wang, Mrunali Manjrekar, Austin Tripp, José Miguel Hernández-Lobato, Connor W. Coley
First submitted to arxiv on: 8 Oct 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 paper proposes a novel acquisition strategy for batched Bayesian optimization (BO) called qPO, which is motivated by pure exploitation. This approach maximizes the probability of including the true optimum in the selected batch, unlike existing strategies that balance exploration and exploitation. The authors differentiate their method from parallel Thompson sampling and demonstrate its effectiveness in model-guided exploration of large chemical libraries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Batched Bayesian optimization can help find the best molecules for molecular design by quickly identifying top-performing compounds from a huge library. Right now, people are trying to figure out how to balance looking at new things and focusing on what works well. This paper proposes a new way to do this that’s all about finding the very best thing. It does this by making sure the group of molecules it looks at includes the absolute best one. This helps avoid having to calculate everything all over again, which can be really tricky. The authors show how their method works better than or just as well as other methods in a special kind of search. |
Keywords
» Artificial intelligence » Optimization » Probability
