arXiv:2512.19373v3 Announce Type: replace-cross Abstract: In developing data-driven modeling methodologies, there is an ongoing need to reconcile the strong predictive performance of opaque black-box models with the transparency required for critical applications. This work introduces an interpretable and computationally tractable regression framework for heterogeneous data by combining response-informed spectral representation learning with localized additive modeling. The method first fits a random Fourier feature regression model and constructs a spectral feature map from the learned amplit
The continuous drive for more interpretable and computationally efficient AI models, especially in critical applications, necessitates ongoing research in areas like generalized additive models and spectral representation learning.
This work addresses the fundamental tension between model predictive power and transparency, a key concern for AI adoption in regulated industries and high-stakes decision-making.
The proposed method offers a new approach to building interpretable regression models for heterogeneous data, potentially improving trust and auditability in AI-driven systems.
- · Machine Learning Researchers
- · Industries requiring interpretable AI (e.g., healthcare, finance)
- · AI ethics and governance initiatives
- · Purely 'black-box' model developers (potentially facing increased scrutiny)
Improved interpretability in specific regression tasks, allowing for better understanding of model decisions.
Reduced barriers to AI adoption in sectors where model explainability is a regulatory or ethical requirement.
Enhanced development of frameworks for 'transparent AI' that could become industry standards, influencing future AI tool design.
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Read at arXiv cs.LG