The paper introduces Conditionally Additive Local Models (CALMs), a new class of interpretable machine learning models that balance the simplicity of Generalized Additive Models (GAMs) with the accuracy of GA$^2$Ms. CALMs allow multiple univariate shape functions per feature, each active in distinct input subregions defined by simple logical conditions on interacting features, thereby capturing interactions while maintaining local additivity. A distillation-based training pipeline identifies homogeneous regions with limited interactions and fits interpretable shape functions via region-aware backfitting, leading to performance that consistently outperforms GAMs and rivals GA$^2$Ms on diverse classification and regression tasks.
By Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos, Konstantinos Tsopelas, Dimitrios Rontogiannis, Giuseppe Casalicchio, Theodore Dalamagas, Christos Diou
arXiv:2606. 19850v1 Announce Type: cross Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability.
By Yasutoshi Kishimoto, Kota Yamanishi, Takuya Matsuda, Shinichi Shirakawa
SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.
By Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables.
arXiv:2607. 07060v1 Announce Type: cross Abstract: Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly.
By Srikumar Krishnamoorthy
The paper introduces Adaptive Derivative-Ordered Random Explanation (ADORE), a unified framework that uses first- and second-order derivatives to capture nonlinear feature interactions and feature-sample dynamics. ADORE combines global feature importance with local sample contributions, quantifying both magnitude and direction of feature impact while identifying critical samples. It achieves computational efficiency via randomized SVD and dynamic sparsity detection, outperforming LIME and SHAP across tabular, text, and image data, and is released as an open-source Python package on GitHub.
By Lemen Chao, Ming Lei, Anran Fanga