Description
Viacheslav Borovitskiy, Alexander Terenin
In applications from drug design to robotics, recent advances in geometric deep learning have demonstrated the value of having specialized methods beyond R^d. Since deep learning tends to require a lot of data, and makes it non-trivial to quantify uncertainty in a way that leads to efficient decision-making, this motivates a need for complementary technical capabilities. In this tutorial, we describe geometric counterparts of data-efficient probabilistic modeling techniques, which are effective in small-data settings, and enable uncertainty to be quantified in a geometry-compatible way. We focus mainly on Gaussian processes and related techniques like Bayesian neural networks. These methods can be used to power automated decision-making systems such as Bayesian optimization, which are used in important applications such as molecular optimization and robotic policy tuning---areas where data-efficiency is key, and geometric properties such as symmetries play a fundamental role. This research area has been rapidly developing, and is starting to become mature enough that comprehensive software packages are becoming available---we will therefore cover both the theory and practical implementation of these methods through software demonstrations, and conclude by showcasing a number of emerging applications.
Slides: https://vab.im/download/2024-07-15-Geometric-Probabilistic-Models-Tutorial/