Deep Tractable Probabilistic Models

Probabilistic models deal with uncertainty in data-driven decision-making and modeling in a principled manner. Recent advances in GPU-accelerated computation have enabled probabilistic models to scale to large and complex data sets. However, as models grow in complexity, efficient exact inference (querying the model) becomes a challenge, hindering their feasibility in high-stakes domains like healthcare. This tutorial aims to introduce participants to Deep Tractable Probabilistic Models (DTPMs), a special class of probabilistic models that balance expressiveness and tractability. Participants will learn about the theoretical foundations, practical implementations, and real-world applications of DTPMs.