Cover Image

Towards Fine-Grained and Verifiable Concept Bottleneck Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fang, Yingying, Xu, Haijie, Wu, Shuang, Anish, Mariathasan, Yang, Guang
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models
by: Narain, Anish, et al.
Published: (2025)

Towards Reasonable Concept Bottleneck Models
by: Kalampalikis, Nektarios, et al.
Published: (2025)

Incremental Residual Concept Bottleneck Models
by: Shang, Chenming, et al.
Published: (2024)

Counterfactual Concept Bottleneck Models
by: Dominici, Gabriele, et al.
Published: (2024)

Causally Reliable Concept Bottleneck Models
by: De Felice, Giovanni, et al.
Published: (2025)

Bayesian Concept Bottleneck Models with LLM Priors
by: Feng, Jean, et al.
Published: (2024)

Do Concept Bottleneck Models Respect Localities?
by: Raman, Naveen, et al.
Published: (2024)

Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
by: Penaloza, Emiliano, et al.
Published: (2025)

Mixture of Concept Bottleneck Experts
by: De Santis, Francesco, et al.
Published: (2026)

Object Centric Concept Bottlenecks
by: Steinmann, David, et al.
Published: (2025)

Learning to Intervene on Concept Bottlenecks
by: Steinmann, David, et al.
Published: (2023)

Learning Concept Bottleneck Models from Mechanistic Explanations
by: De Santis, Antonio, et al.
Published: (2026)

Hoeffding Concept Bottleneck Models with Applications to Overhead Images
by: Bénard, Clément, et al.
Published: (2026)

An Imperfect Verifier is Good Enough: Learning with Noisy Rewards
by: Plesner, Andreas, et al.
Published: (2026)

Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
by: Sun, Ao, et al.
Published: (2024)

SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability
by: Zhang, Hanwei, et al.
Published: (2026)

Relational Concept Bottleneck Models
by: Barbiero, Pietro, et al.
Published: (2023)

Credal Concept Bottleneck Models: Structural Separation of Epistemic and Aleatoric Uncertainty
by: Mukherjee, Tanmoy, et al.
Published: (2026)

GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
by: Rivera, Oscar, et al.
Published: (2026)

Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation
by: Fang, Yingying, et al.
Published: (2024)

Editable Concept Bottleneck Models
by: Hu, Lijie, et al.
Published: (2024)

Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
by: Niu, Yue, et al.
Published: (2026)

An Analysis of Concept Bottleneck Models: Measuring, Understanding, and Mitigating the Impact of Noisy Annotations
by: Park, Seonghwan, et al.
Published: (2025)

Discovering the Representation Bottleneck of Graph Neural Networks
by: Wu, Fang, et al.
Published: (2022)

Process-Guided Concept Bottleneck Model
by: Asiyabi, Reza M., et al.
Published: (2026)

Zero-shot Concept Bottleneck Models
by: Yamaguchi, Shin'ya, et al.
Published: (2025)

Semi-supervised Concept Bottleneck Models
by: Hu, Lijie, et al.
Published: (2024)

Prototype-Grounded Concept Models for Verifiable Concept Alignment
by: Colamonaco, Stefano, et al.
Published: (2026)

Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
by: Xu, Xinyue, et al.
Published: (2024)

Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
by: Lin, Guang, et al.
Published: (2026)

Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models
by: Singhi, Nishad, et al.
Published: (2024)

CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal Reasoning
by: Wang, Yongxin, et al.
Published: (2025)

RainSeer: Fine-Grained Rainfall Reconstruction via Physics-Guided Modeling
by: Chen, Lin, et al.
Published: (2025)

Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck
by: Yang, Hao, et al.
Published: (2024)

Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
by: Choi, Jihye, et al.
Published: (2024)

HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention
by: Xu, Yufei, et al.
Published: (2026)

Deconstructing Generative Diversity: An Information Bottleneck Analysis of Discrete Latent Generative Models
by: Wu, Yudi, et al.
Published: (2025)

From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction
by: Sun, Ziyu, et al.
Published: (2024)

SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
by: Zhao, Yanxiao, et al.
Published: (2025)

FT-Dojo: Towards Autonomous LLM Fine-Tuning with Language Agents
by: Li, Qizheng, et al.
Published: (2026)