Concept Bottleneck Models Information Guide

  1. Introduction of Concept Bottleneck Models
  2. Core Information
  3. History
  4. Full Guide
  5. Summary

Introduction of Concept Bottleneck Models

Concept Bottleneck Models (Paper Explained) News
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Core Information

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History

Information Selective Concept Bottleneck Models Without Predefined Concepts (TMLR 2025) Guide
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[CVPR 2025] Hybrid Concept Bottleneck Models
[CVPR 2025] Hybrid Concept Bottleneck Models
Revisiting Discover-then-Name Concept Bottleneck Models: A Reproducibility Study
Revisiting Discover-then-Name Concept Bottleneck Models: A Reproducibility Study
Limitation in Concept Bottleneck Models - Residuals and Vector Disentanglement | Konghao Zhao
Limitation in Concept Bottleneck Models - Residuals and Vector Disentanglement | Konghao Zhao
DCBM: Data-efficient Concept Bottleneck Models @ ICML 2025
DCBM: Data-efficient Concept Bottleneck Models @ ICML 2025
XAI : Explainable by Design — Concept-Bottleneck Models for Trustworthy Credit Scoring
XAI : Explainable by Design — Concept-Bottleneck Models for Trustworthy Credit Scoring
There Was Never a Bottleneck in Concept Bottleneck Models (Antonio Almudévar)
There Was Never a Bottleneck in Concept Bottleneck Models (Antonio Almudévar)
Bayesian Concept Bottleneck Models with LLM Priors (Jean Feng)
Bayesian Concept Bottleneck Models with LLM Priors (Jean Feng)
Learning Bottleneck Concepts in Image Classification
Learning Bottleneck Concepts in Image Classification
Mixture of Concept Bottleneck Experts (Francesco De Santis) - Interpretable Deep Learning Seminars
Mixture of Concept Bottleneck Experts (Francesco De Santis) - Interpretable Deep Learning Seminars
Inherent Interpretability via Language Model Guided Bottleneck Design  Mark Yatskar Source
Inherent Interpretability via Language Model Guided Bottleneck Design Mark Yatskar Source
9 Min Intro to Rashomon Slice for Concept Bottleneck Models (TMLR 2026)
9 Min Intro to Rashomon Slice for Concept Bottleneck Models (TMLR 2026)

Full Guide

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Last Updated: September 30, 2026

Summary

[CVPR 2026] CBM-Suite | Rethinking Concept Bottleneck Models: From Pitfalls to Solutions Guide
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Summary

arxiv.org/abs/2007.04612 Abstract: We seek to learn Addressing High-level Concepts Limitation in Happy to present our joint work on Interpretability published at ICML 2025. Authors: Katharina Prasse*, Patrick Knab*, Sascha ... This presentation was produced for ANA505 Assignment 4 at National University, course AI Optimization Topics. Interpretable Deep Learning Seminars Abstract Standard Paper: arxiv.org/abs/2304.10131 Code: github.com/wbw520/BotCL. ... experiment um and then we compare against some other kinds of um attempts at generalizing um

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