Summary of Building Trust in Black-box Optimization: a Comprehensive Framework For Explainability, by Nazanin Nezami et al.
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability
by Nazanin Nezami, Hadis Anahideh
First submitted to arxiv on: 18 Oct 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 paper addresses the challenge of optimizing black-box functions within limited evaluation budgets in real-world applications. Surrogate Optimization (SO) is a common approach, but its proprietary nature due to complex surrogate models and acquisition functions can lead to a lack of explainability and transparency. The existing literature has focused on enhancing convergence to global optima, but the practical interpretation of new strategies remains underexplored, especially in batch evaluation settings. This paper proposes Inclusive Explainability Metrics for Surrogate Optimization (IEMSO), a set of model-agnostic metrics designed to enhance transparency, trustworthiness, and explainability of SO approaches. The proposed metrics provide both intermediate and post-hoc explanations to practitioners before and after expensive evaluations. The four primary categories of metrics target specific aspects of the SO process: Sampling Core Metrics, Batch Properties Metrics, Optimization Process Metrics, and Feature Importance. Experimental evaluations demonstrate the potential of the proposed metrics across different benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make complex optimization problems more transparent and trustworthy. Right now, when we use a special type of optimization called Surrogate Optimization (SO), it’s hard to know what’s really going on inside the computer. The creators of SO didn’t make it easy for people to understand why their solutions are good or bad. This paper fixes that problem by introducing new ways to measure how well SO is doing. These metrics help us see what’s happening at different stages, like when we’re choosing which experiments to run and when we’re trying to find the best solution. By using these metrics, we can make better decisions and trust the results more. |
Keywords
* Artificial intelligence * Optimization
