Introduces synthetic benchmarks for concept bottleneck models that control data modality, concept choice, annotation quality, and completeness to evaluate performance in decision support and automation.
Concept-based unsupervised domain adaptation.arXiv preprint arXiv:2505.05195, 2025
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MCBM nests concepts hierarchically to enable multi-granularity inference in a single model, reducing expected intervention costs to O(log K) with monotonic performance gains.
citing papers explorer
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Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models
Introduces synthetic benchmarks for concept bottleneck models that control data modality, concept choice, annotation quality, and completeness to evaluate performance in decision support and automation.
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Matryoshka Concept Bottleneck Models
MCBM nests concepts hierarchically to enable multi-granularity inference in a single model, reducing expected intervention costs to O(log K) with monotonic performance gains.