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A paper introducing a generator-based evaluation framework to study how Concept Bottleneck Models and prototype-based models respond to geometric-latent versus semantic-concept perturbations, arguing that interpretability does not inherently confer robustness but instead redistributes sensitivity across perturbation spaces.
ReCBM proposes an uncertainty-gated relational reasoning framework for Concept Bottleneck Models, introducing concept relations like co-occurrence, implication, and exclusion to recover unreliable or missing concept states and improve interpretability and downstream predictions.
Introduces Hoeffding Concept Bottleneck Models (HCBM), a nonlinear and sparse aggregation of concept scores using Hoeffding functional decomposition of gradient-boosted trees, for improved explainability and accuracy in classification and object detection tasks, with applications to overhead images.
This paper proposes CLIF, a method using influence functions to interpret NLP models at both sample and concept levels within Concept Bottleneck Models, enabling transparent debugging and concept-level analysis.
This paper proposes a fine-grained concept bottleneck model framework that grounds each concept in localized visual evidence, enabling direct verification of concept correctness and improving transparency in medical imaging tasks.