Summary of Explainable Bayesian Optimization, by Tanmay Chakraborty et al.
Explainable Bayesian Optimization
by Tanmay Chakraborty, Christin Seifert, Christian Wirth
First submitted to arxiv on: 24 Jan 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 This research proposes a novel approach called TNTRules to improve Bayesian optimization (BO) by providing interpretable optimization techniques for real-world applications. The authors highlight that current explainable AI (XAI) methods are not tailored for optimization, leading to a gap in the collaborative tuning process between human experts and BO. To address this issue, TNTRules uses multiobjective optimization to produce high-quality explanations, outperforming state-of-the-art XAI methods on benchmark and real-world hyperparameter optimization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to make Bayesian optimization more understandable for humans. Right now, when we use computers to optimize things like the best settings for a machine learning model, the computer’s decisions might not match what a human expert would do because of some simplifications and errors. To bridge this gap, the authors create TNTRules, a system that explains its decisions in a clear way using multiple goals at once. This helps trust between humans and computers when working together to find the best settings. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning * Optimization
