24 citations · 147 across the 75 of their papers we have counts for
17 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…
The Pitfalls of KV Cache Compression
Alex Chen, Renato Geh, Aditya Grover +2
KV cache compression promises increased throughput and efficiency with negligible loss in performance. While the gains in throughput are indisputable and recent literature has inde…
Algorithms for Optimizing Acyclic Queries
Zheng Luo, Wim Van den Broeck, Guy Van den Broeck +1
Most research on query optimization has centered on binary join algorithms like hash join and sort-merge join. However, recent years have seen growing interest in theoretically opt…
Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein +3
Property-based testing validates software against an executable specification by evaluating it on randomly generated inputs. The standard way that PBT users generate test inputs is…
Zero-Variance Gradients for Variational Autoencoders
Zilei Shao, Anji Liu, Guy Van den Broeck
Training deep generative models like Variational Autoencoders (VAEs) requires propagating gradients through stochastic latent variables, which introduces estimation variance that c…