Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis
Nyi Nyi Aung, Heepeom Shin, Abigail Lawlor, Adrian Stein
stat.ML, cs.LG, stat.CO
Submitted: 2026-07-17
Code: https://github.com/NyiNyi-14/GT_MOHA
License: http://creativecommons.org/licenses/by/4.0/
The gist: This work presents a game-theoretic framework for interpretable hyperparameter-objective interaction analysis rather than proposing a new optimization algorithm.
Terminology
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.
Sources
- Hyperparameter Optimization: A Spectral Approach
- Hyperparameter Importance Analysis for Multi-Objective AutoML
- A Survey on Uncertainty Quantification Methods for Deep Learning
- HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
- shapiq: Shapley Interactions for Machine Learning
- On Shapley value for measuring importance of dependent inputs
- Shapley effects for sensitivity analysis with correlated inputs: comparisons with Sobol' indices, numerical estimation and applications
- Shapley Effect Estimation using Polynomial Chaos
- Derivative-based Shapley value for global sensitivity analysis and machine learning explainability
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