Probabilistic Models

Graph-Induced Sum-Product Networks have been (finally!) accepted at ICLR 2024! Blog post coming in the following months… stay tuned! Special thanks to Mathias Niepert for his relentless support =).

We propose a probabilistic model that bridges graph machine learning and sum-product networks to tractably answer probabilistic queries. Our Graph-induced Sum-Product Network can solve unsupervised and supervised graph tasks and it is especially effective in modeling the missing data distribution as well as exploiting unlabeled data in a scarce supervision scenario.

Infinite Contextual Graph Markov Model

Graph Mixture Density Network

Deep Bayesian Graph Networks & Low-resource Classification