Cover Image

Deferring Concept Bottleneck Models: Learning to Defer Interventions to Inaccurate Experts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pugnana, Andrea, Massidda, Riccardo, Giannini, Francesco, Barbiero, Pietro, Zarlenga, Mateo Espinosa, Pellungrini, Roberto, Dominici, Gabriele, Giannotti, Fosca, Bacciu, Davide
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
by: Zarlenga, Mateo Espinosa, et al.
Published: (2025)

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

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

AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
by: Dominici, Gabriele, et al.
Published: (2024)

Interpretable and Fair Mechanisms for Abstaining Classifiers
by: Lenders, Daphne, et al.
Published: (2025)

Interpretable and Fair Mechanisms for Abstaining Classifiers
by: Lenders, Daphne, et al.
Published: (2024)

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

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
by: Dominici, Gabriele, et al.
Published: (2024)

A Causal Framework for Evaluating Deferring Systems
by: Palomba, Filippo, et al.
Published: (2024)

Mathematical Foundation of Interpretable Equivariant Surrogate Models
by: Colombini, Jacopo Joy, et al.
Published: (2025)

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
by: Ciaperoni, Martino, et al.
Published: (2026)

Interpretable Neural-Symbolic Concept Reasoning
by: Barbiero, Pietro, et al.
Published: (2023)

If Concept Bottlenecks are the Question, are Foundation Models the Answer?
by: Debole, Nicola, et al.
Published: (2025)

Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)

Neural Interpretable Reasoning
by: Barbiero, Pietro, et al.
Published: (2025)

Actionable Interpretability Must Be Defined in Terms of Symmetries
by: Barbiero, Pietro, et al.
Published: (2026)

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

AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
by: Punzi, Clara, et al.
Published: (2024)

Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
by: Debot, David, et al.
Published: (2025)

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

Foundations of Interpretable Models
by: Barbiero, Pietro, et al.
Published: (2025)

Learning-to-Defer with Expert-Conditional Advice
by: Montreuil, Yannis, et al.
Published: (2026)

Online Learning-to-Defer with Varying Experts
by: Duy, Dang Hoang, et al.
Published: (2026)

Interpretable Concept-Based Memory Reasoning
by: Debot, David, et al.
Published: (2024)

Understanding Inter-Concept Relationships in Concept-Based Models
by: Raman, Naveen, et al.
Published: (2024)

DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Models
by: He, Chengbo, et al.
Published: (2025)

Principled Approaches for Learning to Defer with Multiple Experts
by: Mao, Anqi, et al.
Published: (2023)

Learning to Defer for Causal Discovery with Imperfect Experts
by: Clivio, Oscar, et al.
Published: (2025)

Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
by: Cinquini, Martina, et al.
Published: (2023)

Hierarchical Concept-based Interpretable Models
by: Hill, Oscar, et al.
Published: (2026)

DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading
by: Wu, Tong, et al.
Published: (2024)

Digging Deeper: Learning Multi-Level Concept Hierarchies
by: Hill, Oscar, et al.
Published: (2026)

DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference
by: Rabbani, Parisa, et al.
Published: (2026)

Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
by: Debole, Nicola, et al.
Published: (2026)

When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
by: Liu, Shuqi, et al.
Published: (2026)

Epistemic Deference to AI
by: Lange, Benjamin
Published: (2025)

Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features
by: Xu, Yi, et al.
Published: (2026)

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer
by: Montreuil, Yannis, et al.
Published: (2026)

Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
by: Alves, Jean V., et al.
Published: (2024)

Learning to Receive Help: Intervention-Aware Concept Embedding Models
by: Zarlenga, Mateo Espinosa, et al.
Published: (2023)