5 papers
How to Marginalize in Causal Structure Learning?
William Zhao, Guy Van den Broeck, Benjie Wang
Bayesian networks (BNs) are a widely used class of probabilistic graphical models employed in numerous application domains. However, inferring the network's graphical structure fro…
Scaling Probabilistic Circuits via Monarch Matrices
Honghua Zhang, Meihua Dang, Benjie Wang +3
Probabilistic Circuits (PCs) are tractable representations of probability distributions allowing for exact and efficient computation of likelihoods and marginals. Recent advancemen…
TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation
Gwen Yidou Weng, Benjie Wang, Guy Van den Broeck
As large language models (LMs) advance, there is an increasing need to control their outputs to align with human values (e.g., detoxification) or desired attributes (e.g., personal…
A Compositional Atlas for Algebraic Circuits
Benjie Wang, Denis Deratani Mauá, Guy Van den Broeck +1
Circuits based on sum-product structure have become a ubiquitous representation to compactly encode knowledge, from Boolean functions to probability distributions. By imposing cons…
Restructuring Tractable Probabilistic Circuits
Honghua Zhang, Benjie Wang, Marcelo Arenas +1
Probabilistic circuits (PCs) are a unifying representation for probabilistic models that support tractable inference. Numerous applications of PCs like controllable text generation…