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

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Editors: Manfred Jaeger, Thomas Dyhre Nielsen

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Contents:

Preliminary

Preface

Thomas D. Nielsen, Manfred Jaeger; Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:

Research Papers

Residual Sum-Product Networks

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

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:

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:

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:

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:

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

Tuning Causal Discovery Algorithms

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

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

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

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

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

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

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

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

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

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

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

Almost No News on the Complexity of MAP in Bayesian Networks

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

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

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

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

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

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

On a possibility of gradual model-learning

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

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

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

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

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

Learning decomposable models by coarsening

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Deep Generalized Convolutional Sum-Product Networks

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

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

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

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

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:

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

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

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

GOBNILP: Learning Bayesian network structure with integer programming

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

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

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

\textttMeDIL: 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

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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