PGM 2024

32 papers

Cauchy Graphical Models

Taurai Muvunza, Yang Li, Kuruoglu Ercan Engin

Serving MPE Queries on Tensor Networks by Computing Derivatives

Maurice Wenig, Hanno Barschel, Joachim Giesen, Andreas Goral, Mark Blacher

Estimating Bounds on Causal Effects Considering Unmeasured Common Causes

Sebastian Bejos, Luis Enrique Sucar, Eduardo F. Morales

Time–Approximation Trade-Offs for Learning Bayesian Networks

Madhumita Kundu, Pekka Parviainen, Saket Saurabh

Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories

Jiřı́ Vomlel, Aleš Kuběna, Martin Šmı́d, Josefina Weinerova

$Ψ$net: Efficient Causal Modeling at Scale

Florian Peter Busch, Moritz Willig, Jonas Seng, Kristian Kersting, Devendra Singh Dhami

Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals

Galia Weidl, Stefan Berres, Anders L. Madsen, Johannes Daxenberger, Annegret Aulbach

An Adaptive Implicit Hitting Set Algorithm for MAP and MPE Inference

Aleksandra Petrova, Javier Larrosa, Emma Rollon

Multi-objective Counterfactuals in Bayesian Classifiers with Estimation of Distribution Algorithms

Daniel Zaragoza-Pellicer, Concha Bielza, Pedro Larrañaga

Enhancing Bayesian Networks with Psychometric Models

I. Pérez, J. Vomlel

Causal Structure Learning With Momentum: Sampling Distributions Over Markov Equivalence Classes

Moritz Schauer, Marcel Wienöbst

Balancing Computational Cost and Accuracy in Inference of Continuous Bayesian Networks

Maarten C. Vonk, Sebastiaan Brand, Ninoslav Malekovic, Thomas Bäck, Alfons Laarman

A Divide and Conquer Approach for Solving Structural Causal Models

Anna Rodum Bjøru, Rafael Cabañas, Helge Langseth, Antonio Salmerón

Geometric No-U-Turn Samplers: Concepts and Evaluation

Bernardo Williams, Hanlin Yu, Marcelo Hartmann, Arto Klami

Learning Causal Markov Boundaries with Mixed Observational and Experimental Data

Konstantina Lelova, Gregory F. Cooper, Sofia Triantafillou

Q-conjugate Message Passing for Efficient Bayesian Inference

Mykola Lukashchuk, Ismail Senoz, Bert de Vries

Soft Learning Probabilistic Circuits

Soroush Ghandi, Benjamin Quost, Cassio de Campos

Kernel-Based Differentiable Learning of Non-Parametric Directed Acyclic Graphical Models

Yurou Liang, Oleksandr Zadorozhnyi, Mathias Drton

Learning Staged Trees from Incomplete Data

Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando

Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity

David Strieder, Mathias Drton

Latent Gaussian Graphical Models with Golazo Penalty

Ignacio Echave-Sustaeta Rodríguez, Frank Röttger

Context-Specific Refinements of Bayesian Network Classifiers

Manuele Leonelli, Gherardo Varando

Counterfactually-Equivalent Structural Causal Modelling Using Causal Graphical Normalizing Flows

Sourabh Balgi, Jose M. Peña, Adel Daoud

Eliminating Variable Order Instability in Greedy Score-Based Structure Learning

Neville K Kitson, Anthony C Constantinou

Modelling Shared Decision Making Interactions using Influence Diagrams

Zeliha Yildirim, Barbaros Yet

AutoCD: Automated Machine Learning for Causal Discovery Algorithms

Gerlise Chan, Tom Claassen, Holger H. Hoos, Tom Heskes, Mitra Baratchi

Fast Arc-Reversal

Cory J. Butz, Anders L. Madsen, Jhonatan S. Oliveira

On the Unlikelihood of D-Separation

Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao

LIMID Quality Control Models for Increasing Failure Rate Processes

Barry R. Cobb

Efficient Detection of Commutative Factors in Factor Graphs

Malte Luttermann, Johann Machemer, Marcel Gehrke

$ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows

Sourabh Balgi, Jose M. Peña, Adel Daoud

Alternative Measures of Direct and Indirect Effects

Jose M. Peña