Probabilistic Concept Bottleneck Models
You can find our tutorial slides here (pdf).
Schedule
Below is the expected (rough) schedule for this tutorial where we indicate next to each section who will be presenting that section’s material (GM for “Giuseppe Marra”, PB for “Pietro Barbiero”, and DD for “David Debot”).
| Part | Topic | Presenter | Main reference |
|---|---|---|---|
| Part 1 | Interpretability Theory | PB | The Standard Interpretable Model |
| Part 2 | Concept representations | GM | What's in the Bottle? A Survey and Roadmap of Concept Bottleneck Models |
| Part 3 | Concept-based task predictions | DD | From statistical relational to neurosymbolic artificial intelligence: A survey |
| Part 4 | PyTorch Concepts | PB | PyC: interpretable models in PyTorch |
Required Background
Our material will assume a basic knowledge of ML (e.g., foundations of supervised learning, experimental design, basic probabilistic modelling, etc.), with particular emphasis on a solid Deep Learning foundation (e.g., tensor calculus, neural networks, backpropagation, etc.). Concepts that may require mathematical tools/expertise beyond those one would expect to be shared among the AI community will be (re)introduced in our tutorial.
Additional Material
If we get access to a recording of our presentation, we will include it in this section as soon as possible.
For a complete bibliography of the topics and works discussed in this tutorial, please refer to our resources section.
Presenters
Citing This Tutorial
If you found this tutorial useful for your research, blogs, or work, please cite it as follows:
Marra G., Barbiero P., and Debot D. (2026). Probabilistic Concept Bottleneck Models. Tutorial at the Conference on Uncertainty in Artificial Intelligence (UAI). https://interpretabledeeplearning.github.io/
Or use the following bibtex entry:
@misc{interpretabledl2026essai,
title = {Probabilistic Concept Bottleneck Models},
author = {Marra G. and Barbiero P. and Debot D.},
year = {2026},
howpublished = {Conference on Uncertainty in Artificial Intelligence (UAI)},
url = {https://interpretabledeeplearning.github.io/}
}