🤖 AI Summary
This work addresses the lack of interpretability in existing methods regarding the influence of hyperparameters in multi-objective optimization. The authors propose a novel game-theoretic framework that, for the first time, integrates Shapley effects with the Pareto front to enable objective-aware global sensitivity analysis, thereby uncovering key hyperparameters and their interactions under different optimization objectives. This approach not only identifies efficient hyperparameter configurations and substantially reduces the search space but also facilitates early-stage model performance estimation. The effectiveness and generalizability of the framework are empirically validated across three distinct neural network architectures and tasks.
📝 Abstract
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.