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ORCIDPGM 2024: De Lindenberg, Nijmegen, the Netherlands
- Johan Kwisthout, Silja Renooij:
International Conference on Probabilistic Graphical Models, De Lindenberg, Nijmegen, the Netherlands, 11-13 September 2024. Proceedings of Machine Learning Research 246, PMLR 2024 - Johan Kwisthout, Silja Renooij:
Preface. i-iv - José M. Peña:
Alternative Measures of Direct and Indirect Effects. 1-19 - Sourabh Balgi, José M. Peña, Adel Daoud:
ρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows. 20-37 - Malte Luttermann, Johann Machemer, Marcel Gehrke:
Efficient Detection of Commutative Factors in Factor Graphs. 38-56 - Barry R. Cobb:
LIMID Quality Control Models for Increasing Failure Rate Processes. 57-69 - Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Huan Wang, Caiming Xiong, Silvio Savarese:
On the Unlikelihood of D-Separation. 70-92 - Cory J. Butz, Anders L. Madsen, Jhonatan de S. Oliveira:
Fast Arc-Reversal. 93-105 - Gerlise Chan, Tom Claassen, Holger H. Hoos, Tom Heskes, Mitra Baratchi:
AutoCD: Automated Machine Learning for Causal Discovery Algorithms. 106-132 - Zeliha Yildirim, Barbaros Yet:
Modelling Shared Decision Making Interactions using Influence Diagrams. 133-146 - Neville Kenneth Kitson, Anthony C. Constantinou:
Eliminating Variable Order Instability in Greedy Score-Based Structure Learning. 147-163 - Sourabh Balgi, José M. Peña, Adel Daoud:
Counterfactually-Equivalent Structural Causal Modelling Using Causal Graphical Normalizing Flows. 164-181 - Manuele Leonelli, Gherardo Varando:
Context-Specific Refinements of Bayesian Network Classifiers. 182-198 - Ignacio Echave-Sustaeta Rodríguez, Frank Röttger:
Latent Gaussian Graphical Models with Golazo Penalty. 199-212 - David Strieder, Mathias Drton:
Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity. 213-230 - Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando:
Learning Staged Trees from Incomplete Data. 231-252 - Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton:
Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models. 253-272 - Soroush Ghandi, Benjamin Quost, Cassio de Campos:
Soft Learning Probabilistic Circuits. 273-294 - Mykola Lukashchuk, Ismail Senöz, Bert de Vries:
Q-conjugate Message Passing for Efficient Bayesian Inference. 295-311 - Konstantina Lelova, Gregory F. Cooper, Sofia Triantafillou:
Learning Causal Markov Boundaries with Mixed Observational and Experimental Data. 312-326 - Bernardo Williams, Hanlin Yu, Marcelo Hartmann, Arto Klami:
Geometric No-U-Turn Samplers: Concepts and Evaluation. 327-347 - Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón:
A Divide and Conquer Approach for Solving Structural Causal Models. 348-360 - Maarten C. Vonk, Sebastiaan Brand, Ninoslav Malekovic, Thomas Bäck, Alfons Laarman, Anna V. Kononova:
Balancing Computational Cost and Accuracy in Inference of Continuous Bayesian Networks. 361-381 - Moritz Schauer, Marcel Wienöbst:
Causal Structure Learning With Momentum: Sampling Distributions Over Markov Equivalence Classes. 382-400 - Iván Pérez, Jirí Vomlel:
Enhancing Bayesian Networks with Psychometric Models. 401-414 - Daniel Zaragoza-Pellicer, Concha Bielza, Pedro Larrañaga:
Multi-objective Counterfactuals in Bayesian Classifiers with Estimation of Distribution Algorithms. 415-426 - Aleksandra Petrova, Javier Larrosa, Emma Rollon:
An Adaptive Implicit Hitting Set Algorithm for MAP and MPE Inference. 427-437 - Galia Weidl, Stefan Berres, Anders L. Madsen, Johannes Daxenberger, Annegret Aulbach:
Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals. 438-451 - Florian Peter Busch, Moritz Willig, Jonas Seng, Kristian Kersting, Devendra Singh Dhami:
Ψnet: Efficient Causal Modeling at Scale. 452-469 - Jirí Vomlel, Ales Kubena, Martin Smíd, Josefina Weinerova:
Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories. 470-485 - Madhumita Kundu, Pekka Parviainen, Saket Saurabh:
Time-Approximation Trade-Offs for Learning Bayesian Networks. 486-497 - Sebastián Bejos, Luis Enrique Sucar, Eduardo F. Morales:
Estimating Bounds on Causal Effects Considering Unmeasured Common Causes. 498-514 - Maurice Wenig, Hanno Barschel, Joachim Giesen, Andreas Goral, Mark Blacher:
Serving MPE Queries on Tensor Networks by Computing Derivatives. 515-527 - Taurai Muvunza, Yang Li, Ercan Engin Kuruoglu:
Cauchy Graphical Models. 528-542
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