Hoeffding Concept Bottleneck Models with Applications to Overhead Images

Hoeffding Concept Bottleneck Models (HCBM) are introduced to offer non-linear and sparse aggregations of concept scores, enhancing the explainability and accuracy of deep learning predictions. This method leverages Hoeffding functional decomposition of gradient-boosted trees to overcome the limitations of existing linear CBMs, which suffer from a large number of concepts and potential information leakage.