Summary of Optimistic Games For Combinatorial Bayesian Optimization with Application to Protein Design, by Melis Ilayda Bal et al.
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design
by Melis Ilayda Bal, Pier Giuseppe Sessa, Mojmir Mutny, Andreas Krause
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
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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 GameOpt method offers a novel game-theoretical approach to Bayesian optimization (BO) for combinatorial problems. Existing BO algorithms are infeasible due to the complexity of optimizing acquisition functions over large, unstructured spaces. GameOpt addresses this by establishing cooperative games between optimization variables and selecting points that are stable configurations (game equilibria) of an upper confidence bound acquisition function. This allows efficient breakdown of complex combinatorial domains into individual decision sets, making GameOpt scalable to large spaces. The method is demonstrated on the challenging protein design problem with four real-world datasets, achieving high performance and rapid discovery of active protein variants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GameOpt is a new way to optimize functions that are difficult to evaluate. It’s like a game where you make good choices to get the best outcome. Normally, finding the best choice would be hard because there are too many options. GameOpt makes it easier by breaking down the problem into smaller parts and making smart decisions about what to try next. This helps find the best answer quickly and efficiently. |
Keywords
* Artificial intelligence * Optimization
