Concept Bottleneck Models, ICML 2020

Code for the paper: Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery. ECCV 2024.

[NeurIPS 24] A new training and evaluation framework for learning interpretable deep vision models and benchmarking different interpretable concept-bottleneck-models (CBMs)

Generalizable AI predicts immunotherapy outcomes across cancers and treatments

[ICLR 2025 Spotlight] This is the official repository for our paper: ''Enhancing Pre-trained Representation Classifiability can Boost its Interpretability''.

Concept bottleneck models for multiview data with incomplete concept sets

[CVPR 2025] Concept Bottleneck Autoencoder (CB-AE) -- efficiently transform any pretrained (black-box) image generative model into an interpretable generative concept bottleneck model (CBM) with minimal concept supervision, while preserving image quality

Code for the paper "CBVLM: Training-free Explainable Concept-based Large Vision Language Models for Medical Image Classification".

[MICCAI 2024] AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis

Papers on CBMs with short descriptions of paper's content

Web Companion for Generalizable AI predicts immunotherapy outcomes across cancers and treatments

This project poses a new methodology for assessing and improving sequential concept bottleneck models (CBMs). The research undertaken in this project builds upon the model proposed by Grange et al., of which I was one of the co-authors.

Semi-supervised Concept Bottleneck Models (SSCBM)