Concept-based models can use controlled 'benign' information leakage to remain accurate and intervenable under real-world concept incompleteness by reframing their training objective.
arXiv preprint arXiv:2402.05945 , year=
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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.
Post-hoc CBMs produce unfaithful concept projections due to covariate shifts and systematic label noise; new metrics are introduced to measure faithfulness separately from accuracy.
Formal concept lattices guide staged, hierarchical concept learning in deep networks to produce more interpretable and semantically structured representations.
citing papers explorer
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In Defense of Information Leakage in Concept-based Models
Concept-based models can use controlled 'benign' information leakage to remain accurate and intervenable under real-world concept incompleteness by reframing their training objective.
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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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On the Faithfulness of Post-Hoc Concept Bottleneck Models
Post-hoc CBMs produce unfaithful concept projections due to covariate shifts and systematic label noise; new metrics are introduced to measure faithfulness separately from accuracy.
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Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Formal concept lattices guide staged, hierarchical concept learning in deep networks to produce more interpretable and semantically structured representations.
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