Summary of Monte Carlo Tree Search Based Space Transfer For Black-box Optimization, by Shukuan Wang et al.
Monte Carlo Tree Search based Space Transfer for Black-box Optimization
by Shukuan Wang, Ke Xue, Lei Song, Xiaobin Huang, Chao Qian
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Bayesian optimization (BO) is a widely used method for optimizing computationally expensive black-box functions. Traditional BO methods converge slowly, leading researchers to explore transfer learning setups to speed up the process. One promising approach is search space transfer, which has shown impressive results in various tasks. However, existing methods either lack adaptive mechanisms or are inflexible, hindering efficient identification of promising search spaces during optimization. This paper proposes MCTS-transfer, a novel method based on Monte Carlo tree search (MCTS), to iteratively divide, select, and optimize in a learned subspace. MCTS-transfer not only provides well-performing search spaces for warm-start but also adapts to similar source tasks to reconstruct the space during optimization. Experimental results demonstrate superior performance of MCTS-transfer compared to other methods on synthetic functions, real-world problems, Design-Bench, and hyper-parameter optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers better at optimizing things that are hard to compute. Right now, computers use something called Bayesian optimization, but it takes a long time to get good results. Researchers want to speed up this process by using ideas from other tasks they’ve already optimized. They’re trying to figure out how to transfer the knowledge they gained from those tasks to new ones. This paper proposes a new method that can do this more efficiently than existing methods. It’s called MCTS-transfer and it uses something called Monte Carlo tree search to find the best way to optimize. The results show that MCTS-transfer is better than other methods at optimizing different types of problems. |
Keywords
* Artificial intelligence * Optimization * Transfer learning
