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Parameter Space Analysis through Guided Visual Interpolations

arXiv 2509.19202 ↗First posted 23 Sep 2025cs.CE cs.LGcited by 0

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Abstract (arXiv)

We propose Parameter Space Analysis through Guided Visual Interpolations (ParamInter), a novel tool for high-dimensional input parameter space analysis by making interpolation towards optimal parameter sets explorable using guided analytics. The interpolation is accompanied by both small multiples in linked views and utilizes t-Distributed Stochastic Neighbor Embedding (t-SNE) representations to show an interpolation overview. ParamInter uses a guided exploration loop focusing on the interpolation towards user-specified target parameters from many output parameters. The exploration process is additionally guided through eXplainable Artificial Intelligence (XAI)-based effect suggestions throughout our tool. ParamInter, compared to prior work, focuses on the integration of state-of-the art effect-based XAI and Uncertainty Quantification (UCQ) approaches for guidance, and introduces an interpolation towards the optimal solution through interpolation between the initial parameter setting and the optimal setting. We also add an interpretability layer for dimensionality-reduced data by displaying our novel interpolation towards the optimum, enhanced by small multiples of the input parameters on top. We demonstrate the direct applicability of our tool on a real-world use case for a blast furnace optimisation process, where a multi-objective problem is solved through modeling and visualisation.

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References · 8

  1. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 129
  2. 2020Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligencecited by 82
  3. 2024Conformal Prediction Sets Improve Human Decision Makingcited by 35
  4. 2023Using AI Uncertainty Quantification to Improve Human Decision-Makingcited by 11
  5. 2023Fully Bayesian Autoencoders with Latent Sparse Gaussian Processescited by 9
  6. 2017GPU-acceleration for Large-scale Tree Boostingcited by 8
  7. 2025A Critical Analysis of the Usage of Dimensionality Reduction in Four Domainscited by 7
  8. 2024Robustness of Explainable Artificial Intelligence in Industrial Process Modellingcited by 2

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