PGM 2024
32 papers
$Ψ$net: Efficient Causal Modeling at Scale
Florian Peter Busch, Moritz Willig, Jonas Seng, Kristian Kersting, Devendra Singh Dhami A Divide and Conquer Approach for Solving Structural Causal Models
Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón AutoCD: Automated Machine Learning for Causal Discovery Algorithms
Gerlise Chan, Tom Claassen, Holger H. Hoos, Tom Heskes, Mitra Baratchi Balancing Computational Cost and Accuracy in Inference of Continuous Bayesian Networks
Maarten C. Vonk, Sebastiaan Brand, Ninoslav Malekovic, Thomas Bäck, Alfons Laarman, Anna V. Kononova Cauchy Graphical Models
Taurai Muvunza, Yang Li, Kuruoglu Ercan Fast Arc-Reversal
Cory J. Butz, Anders L. Madsen, Jhonatan S. Oliveira Geometric No-U-Turn Samplers: Concepts and Evaluation
Bernardo Williams, Hanlin Yu, Marcelo Hartmann, Arto Klami Learning Staged Trees from Incomplete Data
Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando On the Unlikelihood of D-Separation
Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Huan Wang, Caiming Xiong, Silvio Savarese Serving MPE Queries on Tensor Networks by Computing Derivatives
Maurice Wenig, Hanno Barschel, Joachim Giesen, Andreas Goral, Mark Blacher Soft Learning Probabilistic Circuits
Soroush Ghandi, Benjamin Quost, Cassio Campos