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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-05-29 · via Proceedings of Machine Learning Research

[edit]

Volume 138: International Conference on Probabilistic Graphical Models, 23-25 September 2020, Hotel Comwell Rebild Bakker, Skørping, Denmark

[edit]

Editors: Manfred Jaeger, Thomas Dyhre Nielsen

[bib][citeproc]

Contents:

  • Preliminary
  • Research Papers
  • Software Demonstrations

Filter Authors: Filter Titles:

Preliminary

Preface

; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:1-4

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Research Papers

Structure Learning from Related Data Sets with a Hierarchical Bayesian Score

Laura Azzimonti, Giorgio Corani, Marco Scutari; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:5-16

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Tuning Causal Discovery Algorithms

Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:17-28

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Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data

Tjebbe Bodewes, Marco Scutari; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:29-40

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Constraing-Based Learning for Continous-Time Bayesian Networks

Alessandro Bregoli, Marco Scutari, Fabio Stella; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:41-52

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Sum-Product Network Decompilation

Cory Butz, Jhonatan S. Oliveira, Robert Peharz; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:53-64

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Solving Multiple Inference by Minimizing Expected Loss

Cong Chen, Jiaqi Yang, Chao Chen, Changhe Yuan; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:65-76

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Efficient Heuristic Search for M-Modes Inference

Cong Chen, Changhe Yuan, Chao Chen; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:77-88

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Supervised Learning with Background Knowledge

Yizuo Chen, Arthur Choi, Adnan Darwiche; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:89-100

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Bayesian network structure learning with causal effects in the presence of latent variables

Kiattikun Chobtham, Anthony C. Constantinou; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:101-112

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Approximating bounded tree-width Bayesian network classifiers with OBDD

Karine Chubarian, György Turán; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:113-124

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Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data

Clavier Pierre, Bouaziz Olivier, Nuel Gregory; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:125-136

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Strudel: Learning Structured-Decomposable Probabilistic Circuits

Meihua Dang, Antonio Vergari, Guy Broeck; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:137-148

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Almost No News on the Complexity of MAP in Bayesian Networks

Cassio P. de Campos; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:149-160

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Contrastive Divergence Learning with Chained Belief Propagation

Ding Fan, Xue Yexiang; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:161-172

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An Efficient Low-Rank Tensors Representation for Algorithms in Complex Probabilistic Graphical Models

Gaspard Ducamp, Philippe Bonnard, Anthony pages = 173-184 Nouy, Pierre-Henri Wuillemin; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:173-184

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Interactive Anomaly Detection in Mixed Tabular Data using Bayesian Networks

Evan Dufraisse, Philippe Leray, Raphaël Nedellec, Tarek Benkhelif; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:185-196

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Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping

Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:197-208

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Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph

Pierre Gillot, Pekka Parviainen; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:209-220

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Kernel-based Approach for Learning Causal Graphs from Mixed Data

Teny Handhayani, James Cussens; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:221-232

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Lifted Query Answering in Gaussian Bayesian Networks

Mattis Hartwig, Ralf Möller; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:233-244

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On a possibility of gradual model-learning

Radim Jiroušek; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:245-256

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Causal Feature Learning for Utility-Maximizing Agents

David Kinney, David Watson; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:257-268

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Lifted Weight Learning of Markov Logic Networks (Revisited One More Time)

Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:269-280

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Prediction of High Risk of Deviations in Home Care Deliveries

Anders L. Madsen, Kristian G. Olesen, Heidi Lynge Løvschall, Nicolaj Søndberg-Jeppesen, Frank Jensen, Morten Lindblad, Mads Lause Mogensen, Trine Søby Christensen; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:281-292

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Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks

Denis Deratani Mauá, Heitor Ribeiro Reis, Gustavo Perez Katague, Alessandro Antonucci; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:293-304

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Discovering cause-effect relationships in spatial systems with a known direction based on observational data

Konrad P Mielke, Tom Claassen, J Huijbregts, Aafke M Schipper, Tom M Heskes; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:305-316

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Learning decomposable models by coarsening

George Orfanides, Aritz Pérez; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:317-328

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Correlated Equilibria for Approximate Variational Inference in MRFs

Luis E. Ortiz, Boshen Wang, Ze Gong; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:329-340

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Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations

Tomáš Pevný, Václav Smídl, Martin Trapp, Ondřej Poláček, Tomáš Oberhuber; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:341-352

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Discriminative Non-Parametric Learning of Arithmetic Circuits

Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:353-364

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Learning Optimal Cyclic Causal Graphs from Interventional Data

Kari Rantanen, Antti Hyttinen, Matti Järvisalo; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:365-376

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Knowledge Transfer for Learning Markov Equivalence Classes

Verónica Rodríguez-López, Luis Enrique Sucar; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:377-388

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Differentiable TAN Structure Learning for Bayesian Network Classifiers

Wolfgang Roth, Franz Pernkopf; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:389-400

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Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:401-412

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A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

Charupriya Sharma, Zhenyu A. Liao, James Cussens, Peter Beek; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:413-424

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A New Perspective on Learning Context-Specific Independence

Yujia Shen, Arthur Choi, Adnan Darwiche; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:425-436

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Constructing a Chain Event Graph from a Staged Tree

Aditi Shenvi, Jim Q Smith; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:437-448

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Dual Formulation of the Chordal Graph Conjecture

Milan Studeny, James Cussens, Vaclav Kratochvil; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:449-460

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Bayesian Network Model Averaging Classifiers by Subbagging

Shouta Sugahara, Itsuki Aomi, Maomi Ueno; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:461-472

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Learning Bayesian Networks with Cops and Robbers

Topi Talvitie, Pekka Parviainen; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:473-484

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Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference

Nazanin Tehrani, Nimar S. Arora, Yucen Lily Li, Kinjal Divesh Shah, David Noursi, Michael Tingley, Narjes Torabi, Sepehr = 485-496, Eric Lippert, Erik Meijer; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:485-496

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Missing Values in Multiple Joint Inference of Gaussian Graphical Models

Veronica Tozzo, Davide Garbarino, Annalisa Barla; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:497-508

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Building Causal Interaction Models by Recursive Unfolding

L. C. van der Gaag, S. Renooij, A. Facchini; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:509-520

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Poset Representations for Sets of Elementary Triplets

L. C. van der Gaag, J. H. Bolt; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:521-532

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Deep Generalized Convolutional Sum-Product Networks

Jos Wolfshaar, Andrzej Pronobis; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:533-544

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Residual Sum-Product Networks

Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:545-556

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Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces

Cen Wan, Alex Freitas; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:557-568

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Hawkesian Graphical Event Models

Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Lingzhou Xue; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:569-580

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Structural Causal Models Are (Solvable by) Credal Networks

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:581-592

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Software Demonstrations

aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python

Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:

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BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks

Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:593-596

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CREDICI: A Java Library for Causal Inference by Credal Networks

Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:597-600

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Probabilistic Graphical Models with Neural Networks in InferPy

Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:601-604

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GOBNILP: Learning Bayesian network structure with integer programming

James Cussens; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:605-608

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CREMA: A Java Library for Credal Network Inference

David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:613-616

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A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital

Anders L. Madsen, Kristian G. Olesen, Jørn Munkhof Møller, Nicolaj Søndberg-Jeppesen, Frank Jensen, Thomas Mulvad Larsen, Per Henriksen, Morten Lindblad, Trine Søby Christensen; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:617-620

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MeDIL: A Python Package for Causal Modelling

Alex Markham, Aditya Chivukula, Moritz Grosse-Wentrup; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:621-624

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PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python

Jonathan Serrano-Pérez, L. Enrique Sucar; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:625-628

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