哪些超参数重要？用于可解释超参数敏感性分析的博弈论框架

中文摘要 AI 辅助

本文提出了一个用于可解释超参数与目标交互分析的博弈论框架，而非新的优化算法。该框架中，利用Shapley效应进行全局敏感性分析，通过帕累托前沿集识别有效的超参数配置并支持早期模型评估。分析揭示了在给定博弈（应用）中哪些参与者（超参数）对不同目标最具影响力。这为目标感知的超参数交互提供了可解释的见解，能指导后续优化、缩小搜索空间并进行早期模型评估。通过三个不同神经网络架构在多目标设置下跨不同问题域的实验验证了该框架的有效性。

英文摘要

This work presents a game-theoretic framework for interpretable hyperparameter-objective interaction analysis rather than proposing a new optimization algorithm. In the proposed framework, Shapley Effects are employed for global sensitivity analysis, while Pareto front sets are utilized to identify effective hyperparameter configurations and support early-stage model evaluation. The resulting analysis reveals which players (hyperparameters) are most influential with respect to different objectives in a given game (application). Consequently, the proposed framework provides interpretable insights into objective-aware hyperparameter interactions, enabling practitioners to guide subsequent optimization, reduce the search space, and perform early-stage model evaluation. The effectiveness of the proposed framework is demonstrated using three distinct neural network architectures across different problem domains under multi-objective settings.

发表机构

• Department of Mechanical and Industrial Engineering（机械与工业工程系）
• Louisiana State University（路易斯安那州立大学）
• Department of Electrical and Computer Engineering（电气与计算机工程系）

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