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Interpretable Causal Discovery via Causal-Effect Constraints
Cixuan Zhang, Guy Van den Broeck, Benjie Wang
Causal discovery aims to uncover the underlying causal relationships given data generated from a system. The goal, however, is not merely to predict causal edges given data, but al…
Breaking the Factorization Barrier in Diffusion Language Models
Ian Li, Zilei Shao, Benjie Wang +3
Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the ``factorization barrier'': the assumption that simultaneously pr…
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…
Probabilistic Circuits for Cumulative Distribution Functions
Oliver Broadrick, William Cao, Benjie Wang +2
A probabilistic circuit (PC) succinctly expresses a function that represents a multivariate probability distribution and, given sufficient structural properties of the circuit, sup…
On the Relationship Between Monotone and Squared Probabilistic Circuits
Benjie Wang, Guy Van den Broeck
Probabilistic circuits are a unifying representation of functions as computation graphs of weighted sums and products. Their primary application is in probabilistic modeling, where…