Research map / Label-efficient and robust learning / Recognition and long-tail
A theoretical design of concept sets: improving the predictability of concept bottleneck models
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Gives a theoretical framework for concept sets in concept bottleneck models (expressiveness and model-aware inductive bias), derives a risk decomposition for CLIP-based CBMs, and proposes an LLM-based concept generation and filtering procedure validated on CIFAR-10 and MetaShift.
Problem
Concept bottleneck models lack a clear understanding of which properties of concept sets determine their predictive performance, especially sample efficiency and out-of-distribution robustness.
Approach
Formalizes concepts as fuzzy membership mappings with two desiderata (expressiveness and model-aware inductive bias), derives a closed-form risk for a linear concept-based model under isotropic inputs that trades off concept set size against misspecification, and uses these results to drive LLM-sampled, entropy- and risk-filtered concept set generation over CLIP embeddings.
Key results
- On CIFAR-10, smaller concept sets (n<100 training samples) help in the small-sample regime while larger concept sets (n>200) win with more data, matching Theorem 1.
- On MetaShift distribution shift, concept embeddings improve accuracy over vanilla CLIP with bigger gains for stronger shifts.
- On 12 MetaShift OOD tasks, the CBM typically beats the non-bottleneck baseline by more than 10% accuracy, sometimes over 50% relative.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a concept representation instead of the direct CLIP embedding | Linear CBM with CLIP ViT-B/32 · MetaShift Cat vs. Dog subpopulation shift | accuracy | Helped |
| Use a concept representation instead of the direct CLIP embedding | CBM with CLIP ViT-B/32 · MetaShift out-of-distribution tasks | accuracy: above 10%; 50% | Helped |
| Use a concept representation instead of the direct CLIP embedding (ablation) | CBM with CLIP ViT-B/32 · CIFAR-10 | generalization performance | Mixed |
| Use a smaller concept set (ablation) | CBM with CLIP ViT-B/32 · CIFAR-10, small-sample regime | generalization performance | Helped |
| Use a larger concept set (ablation) | CBM with CLIP ViT-B/32 · CIFAR-10, larger-sample regime | generalization performance | Helped |
| Use concept representations instead of direct CLIP embeddings | CBM with k-nearest neighbors or decision trees · MetaShift Cat vs. Dog subpopulation shift | accuracy | Helped |
Limitations
- Theory assumes isotropic input distribution and linear output models.
- Some settings cannot reach low misspecification with any human-chosen concept set, so CBMs need extensions such as residual fitting.
- Experiments are limited to CIFAR-10 and MetaShift with a single CLIP backbone.
Experimental setup
Datasets: CIFAR-10 MetaShift CUB-200-2011 Food-101 DTD CINIC-10
Benchmarks: CIFAR-10 MetaShift CINIC-10
Metrics: accuracy risk (MSE)
Baselines: vanilla CLIP ViT-B/32 embedding linear probe decision tree on CLIP embedding k-nearest neighbors on CLIP embedding
Builds on: Concept Bottleneck Models CLIP MetaShift Label-free CBM
Closest papers by meaning
- Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations (ICLR 2024 · similarity 0.91)
- Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions (NeurIPS 2025 · similarity 0.91)
- Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models (NeurIPS 2026 · similarity 0.90)
- Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations (NeurIPS 2026 · similarity 0.90)
- Hybrid Concept Bottleneck Models (CVPR 2025 · similarity 0.89)
- Stochastic Concept Bottleneck Models (NeurIPS 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.