Summary of Indirect Query Bayesian Optimization with Integrated Feedback, by Mengyan Zhang et al.
Indirect Query Bayesian Optimization with Integrated Feedback
by Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong, Seth Flaxman, Dino Sejdinovic
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Indirect Query Bayesian Optimization (IQBO), a novel approach to optimize unknown functions when direct feedback is not accessible due to privacy, hardware, or computational constraints. IQBO leverages conditional expectations of the unknown function f to adaptively query and observe in transformed spaces. The Conditional Max-Value Entropy Search (CMES) acquisition function and hierarchical search algorithm are introduced to address this setting efficiently. Regret bounds are provided for the proposed methods, and their effectiveness is demonstrated on simulated optimization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to find the best solution to an unknown problem when you can’t get direct feedback. This happens in real-life situations where privacy or computer limitations prevent you from getting immediate answers. The team creates a framework called Indirect Query Bayesian Optimization (IQBO) that uses statistical methods to adaptively search for the best solution. They also introduce new algorithms to make this process more efficient. The paper shows that their approach works well on simulated examples, which is an important step towards solving real-world problems. |
Keywords
» Artificial intelligence » Optimization
