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This paper introduces Hoeffding Concept Bottleneck Models (HCBM), a novel approach that replaces linear concept aggregation with non-linear, sparse functions derived from gradient-boosted trees to improve explainability and reduce information leakage in computer vision. By leveraging prime implicants for compact predictions, the method demonstrates superior robustness against interconcept leakage compared to existing sparse linear models, particularly in high-stakes overhead imaging scenarios. The work advances the theoretical and practical boundaries of interpretable deep learning by addressing the inherent non-linearity between concepts and output logits.
Hoeffding Concept Bottleneck Models with Applications to Overhead Images
arXiv:2606.00082v2 Announce Type: replace-cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stakes decisions. Concept bottleneck models (CBM) have recently shown promising performance to provide explainable and accurate predictions for classification problems, based on a bottleneck of high-level concepts. Existing CBM methods rely on a linear aggregation of the concept scores to compute predictions. However, a large number of concepts is often used in this linear approach, which undermines explainability and favors information leakage. In general, the underlying relation between concepts and output logits is not linear. Therefore, we introduce Hoeffding Concept Bottleneck Models (HCBM), which build on the Hoeffding functional decomposition of gradient-boosted trees to provide non-linear and sparse aggregations of concept scores, and generate compact predictions using prime implicants. HCBM have strong theoretical properties of robustness to interconcept leakage, and outperform sparse linear CBM in practice, as shown in extensive experiments. Beyond classification, HCBM can be adapted to object detection, and we focus on a challenging case with overhead images to show the high performance of HCBM in these settings.
Abstract:Explainability of deep learning algorithms is critical for computer-vision applications with high-stakes decisions. Concept bottleneck models (CBM) have recently shown promising performance to provide explainable and accurate predictions for classification problems, based on a bottleneck of high-level concepts. Existing CBM methods rely on a linear aggregation of the concept scores to compute predictions. However, a large number of concepts is often used in this linear approach, which undermines explainability and favors information leakage. In general, the underlying relation between concepts and output logits is not linear. Therefore, we introduce Hoeffding Concept Bottleneck Models (HCBM), which build on the Hoeffding functional decomposition of gradient-boosted trees to provide non-linear and sparse aggregations of concept scores, and generate compact predictions using prime implicants. HCBM have strong theoretical properties of robustness to interconcept leakage, and outperform sparse linear CBM in practice, as shown in extensive experiments. Beyond classification, HCBM can be adapted to object detection, and we focus on a challenging case with overhead images to show the high performance of HCBM in these settings.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
arXiv:2606.00082 [cs.LG]
(or arXiv:2606.00082v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.00082 Focus to learn more arXiv-issued DOI via DataCite
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