1 citations · 1 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
Learning Tractable Distributions Of Language Model Continuations
Gwen Yidou-Weng, Ian Li, Anji Liu +4
Controlled generation imposes sequence-level constraints (syntax, style, safety) that depend on future tokens, making exact conditioning of an autoregressive LM intractable. Tracta…
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…