Tutorial on Geometric Probabilistic Models

ProbAI 2025

Slides

You can find the slides here.

Practice

Notebook #1 [Google Colab] can be found here.
You can show/hide solution by clicking here.

Notebook #2 [Google Colab] can be found here.
You can show/hide solution by clicking here.

Extended Notebooks

Euclidean probabilistic models is an extension of Notebook #1 which features Bayesian neural networks and Gaussian process models shown in the slides but not considered in Notebook #1 itself.
It can be found here.

Geometric probabilistic models is an extension of Notebook #2 which features Bayesian graph CNNs and graph Gaussian processes shown in the slides but not considered in Notebook #2 itself.
It can be found here.
Note: with default Google Colab GPUs, this notebook may take a long time (hours) to run.

Materials

Resources on geometric deep learning (including the proto-book): https://geometricdeeplearning.com.
Also, A Gentle Introduction to Graph Neural Networks.

A simple benchmark suite for geometric probabilistic models: https://github.com/vabor112/pems-regression.

Analogs of RBF (and Matérn) kernels on various geometric spaces: https://geometric-kernels.github.io/.

References: as part of the slides.