9/15/2025 - Invited talk: Cory J. Butz - University of Regina, Canada

Abstract: 

Probabilistic Sentential Decision Diagrams (PSDDs) are an elegant framework for learning from and reasoning about data. They provide tractable representations of discrete probability distributions over structured spaces defined by massive logical constraints, can be compiled from graphical models such as Bayesian networks, and can be learned from both complete and incomplete datasets. The effectiveness of PSDDs has been demonstrated in numerous real real-world applications, including learning user preferences, anomaly detection, and route distribution modelling. 

In this seminar, we present three novel contributions to PSDD learning and inference. First, rather than traversing the entire PSDD during parameter learning for each dataset example, we exploit determinism to focus only on the relevant portion of the model. Second, we show how to prune deterministic computation in inference, thereby avoiding the need to propagate probabilities through every node in the network for each query. Third, we introduce a technique that parallelizes a single circuit evaluation, rather than parallelizing individual multiplications or layer layer-wise inference. For both learning and inference, experimental results on benchmark PSDDs from diverse application domains demonstrate state state-of-the-art performance.

Who: Cory J. Butz, University of Regina, Canada

Profile: personal webpage

When: 02:00 p.m. Monday, September 15, 2025

Where: The session will occur physically at the Institute of Information Theory and Automation (UTIA) in room 203.

Language: English

Invitation: here

As part of the Strategy AV21