ESSAI 2026 | Interpretable Deep Learning

Foundations of Concept-Based Interpretable Deep Learning

Tutorial to be held at ESSAI 2026
Vienna, Austria
July 6-10, 2026

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 (PB for “Pietro Barbiero” and GM for “Giuseppe Marra”).

Day	Topic	Presenter	Main reference
Day 1	Interpretability Theory	PB	The Standard Interpretable Model
Day 2	Blueprint for interpretable models + interpretability in PyTorch	PB	PyC: interpretable models in PyTorch
Day 3	Concept Representations	GM	What’s in the Bottle? A Survey and Roadmap of Concept Bottleneck Models
Day 4	Concept-based Task Predictors	GM	From statistical relational to neurosymbolic artificial intelligence: A survey

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

Giuseppe Marra

KU Leuven, BE

Pietro Barbiero

IBM Research, Switzerland

Citing This Tutorial

If you found this tutorial useful for your research, blogs, or work, please cite it as follows:

Marra G. and Barbiero P. (2026). Foundations of Concept-Based Interpretable Deep Learning. Advanced course at the European Summer School on Artificial Intelligence (ESSAI). https://interpretabledeeplearning.github.io/

Or use the following bibtex entry:

@misc{interpretabledl2026essai,
  title        = {Foundations of Concept-Based Interpretable Deep Learning (ESSAI-2026)},
  author       = {Marra G. and Barbiero P.},
  year         = {2026},
  howpublished = {Advanced course at the European Summer School on Artificial Intelligence (ESSAI)},
  url          = {https://interpretabledeeplearning.github.io/}
}