用博弈论分析超参对多目标的影响，让调参更透明可解释。
Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis
- 基于谢尔普利效应做全局敏感性分析，量化各超参影响。
- 通过帕累托前沿识别有效超参组合，支持早期模型评估。
- 适合需理解超参作用的开发者，提升调参效率与可解释性。
本文提出一种基于博弈论的可解释超参数-目标交互分析框架，不涉及新优化算法。该框架采用谢尔普利效应进行全局敏感性分析，利用帕累托前沿集识别有效超参数配置，支持早期阶段模型评估。分析结果揭示在特定任务中哪些超参数（玩家）对不同目标（游戏）最具影响力。该框架提供面向目标的超参数交互可解释洞察，帮助实践者指导后续优化、缩小搜索空间并实现早期评估。在三种不同神经网络架构、跨多个问题领域的多目标设置下验证了其有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。