# Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis

arXiv: https://arxiv.org/abs/2607.15884 · HTML: https://mlsanity.com/paper/2607.15884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [A Survey on Uncertainty Quantification Methods for Deep Learning](https://mlsanity.com/paper/2302.13425/) (2023)
- [shapiq: Shapley Interactions for Machine Learning](https://mlsanity.com/paper/2410.01649/) (2024)
- [Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits](https://mlsanity.com/paper/2405.03875/) (2024)
- [Hyperparameter Optimization: A Spectral Approach](https://mlsanity.com/paper/1706.00764/) (2017)
- [Hyperparameter Importance Analysis for Multi-Objective AutoML](https://mlsanity.com/paper/2405.07640/) (2024)
- [Powershap: A Power-full Shapley Feature Selection Method](https://mlsanity.com/paper/2206.08394/) (2022)
- [Computing Shapley Effects for Sensitivity Analysis](https://mlsanity.com/paper/2002.12024/) (2020)
- [HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization](https://mlsanity.com/paper/2502.01276/) (2025)
- [Derivative-based Shapley value for global sensitivity analysis and machine learning explainability](https://mlsanity.com/paper/2303.15183/) (2023)
- [Shapley Based Residual Decomposition for Instance Analysis](https://mlsanity.com/paper/2305.18818/) (2023)
- [Shapley Effect Estimation using Polynomial Chaos](https://mlsanity.com/paper/2301.04315/) (2023)

## Abstract (arXiv)

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.