2025 "interpretable machine learning" Papers

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Causally Reliable Concept Bottleneck Models

Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis et al.

NeurIPS 2025posterarXiv:2503.04363
5
citations

From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks

Jie Yang, Yuwen Wang, Kaixuan Chen et al.

ICLR 2025posterarXiv:2505.00364
3
citations

MIX: A Multi-view Time-Frequency Interactive Explanation Framework for Time Series Classification

Viet-Hung Tran, Ngoc Phu Doan, Zichi Zhang et al.

NeurIPS 2025poster