Summary of Multi-fidelity Bayesian Optimization with Across-task Transferable Max-value Entropy Search, by Yunchuan Zhang et al.
Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
by Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Signal Processing (eess.SP)
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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 A novel information-theoretic acquisition function balances the need for optimizing current tasks with collecting transferable knowledge for future tasks, enhancing efficiency in multi-fidelity black-box optimization. The approach utilizes particle-based variational Bayesian updates to transfer Gaussian process surrogate model distributions across tasks. Theoretical analysis and experimental results on synthetic and real-world examples demonstrate improved optimization efficiency as more tasks are processed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of solving a series of optimization problems, where the goal is to find the best solution while also considering what we can learn from one task that can help with others. The method uses a special type of mathematical model called a Gaussian process and updates it by combining information from different tasks. This helps optimize future tasks faster and more efficiently. |
Keywords
* Artificial intelligence * Optimization
