Conference programme

Wednesday, September 23



08.50 – 9.00


09.00 – 10.30

Plenary presentations: Learning and Classifiers. Session chair: Pedro Larrañaga

·      Shouta Sugahara, Itsuki Aomi and Maomi Ueno: Bayesian Network Model Averaging Classifiers by Subbagging

·      Cen Wan and Alex A. Freitas: Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces

·      Wolfgang Roth and Franz Pernkopf: Differentiable TAN Structure Learning for Bayesian Network Classifiers

·      Aditi Shenvi and Jim Q. Smith: Constructing a Chain Event Graph from a Staged Tree

·      Evan Dufraisse, Philippe Leray, Raphaël Nedellec and Tarek Benkhelif: Interactive Anomaly Detection in Mixed Tabular Data using Bayesian Networks

·      Topi Talvitie and Pekka Parviainen: Learning Bayesian Networks with Cops and Robbers

10.30 – 11.00


11.00 – 12.30

Plenary presentations: Complexity and Inference. Session chair: Silja Renooij

·      Mattis Hartwig and Ralf Möller: Lifted Query Answering in Gaussian Bayesian Networks

·      Ondřej Kuželka, Vyacheslav Kungurtsev and Yuyi Wang: Lifted Weight Learning of Markov Logic Networks (Revisited Once More Time)

·      Cassio P. de Campos: Almost No News on the Complexity of MAP in Bayesian Networks

·      Veronica Tozzo, Davide Garbarino and Annalisa Barla: Missing Values in Multiple Joint Inference of Gaussian Graphical Models

·      Gaspard Ducamp, Philippe Bonnard, Anthony Nouy and Pierre-Henri Wuillemin: An Efficient Low-Rank Tensors Representations for Algorithms in Complex Probabilistic Graphical Models

·      Anders L. Madsen, Kristian G. Olesen, Heidi Lynge Løvschall, Nicolaj Søndberg-Jeppesen, Frank Jensen, Morten Lindblad, Mads Lause Mogensen and Trine Søby Christensen: Prediction of High Risk of Deviations in Home Care Deliveries

12.30 – 13.30


13.30 – 14.30

Poster presentations

14.30 – 16.00

Plenary presentations: Learning. Session chair: Fabio Stella

·      Charupriya Sharma, Zhenyu Liao, James Cussens and Peter van Beek: A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

·      Fan Ding and Yexiang Xue: Contrastive Divergence Learning with Chained Belief Propagation

·      Jos van de Wolfshaar and Andrzej Pronobis: Deep Generalized Convolutional Sum-Product Networks

·      Nandini Ramanan, Mayukh Das, Kristian Kersting and Sriraam Natarajan: Discriminative Non-Parametric Learning of Arithmetic Circuits

·      Jonathan Serrano-Pérez and Enrique Sucar: PGM PyLib: A Toolkit for Probabilistic Graphical Models in Python

·      Verónica Rodríguez-López and Luis Enrique Sucar: Knowledge Transfer for Learning Markov Equivalence Classes

16.00 – 16.30


16.30 – 18.00

Plenary presentations: Geographic theme. Session chair: Alessandro Antonucci

·      Nazanin Tehrani, Nimar Arora, Yucen Li, Kinjal Shah, David Noursi, Michael Tingley, Narjes Torabi, Sepehr Masouleh, Eric Lippert, and Erik Meijer: Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference

·      Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian and Lingzhou Xue: Hawkesian Graphical Event Models

·      Luis Ortiz, Boshen Wang and Ze Gong: Correlated Equilibria for Approximate Variational Inference in MRFs

·      Yizuo Chen, Arthur Choi and Adnan Darwiche: Supervised Learning with Background Knowledge

·      Yujia Shen, Arthur Choi and Adnan Darwiche: A New Perspective on Learning Context-Specific Independe

·      Meihua Dang, Antonio Vergari and Guy Van den Broeck: Strudel: Learning Structured-Decomposable Probabilistic Circuits

18.30 – 20.00


20.00 – 21.30

Poster presentations


Thursday, September 24



09.00 – 10.30

Plenary presentations: Learning. Session chair: Concha Bielza

·      Tjebbe Bodewes and Marco Scutari: Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data

·      Radim Jiroušek: On a Possibility of Gradual Model-Learning

·      Alessandro Bregoli, Marco Scutari and Fabio Stella: Constraint-Based Learning for Continuous-Time Bayesian Networks (PGM 2020 Best student paper)

·      George Orfanides and Aritz Pérez: Learning decomposable models by coarsening

·      Pierre Gillot and Pekka Parviainen: Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph

·      Laura Azzimonti, Giorgio Corani and Marco Scutari: Structure Learning from Related Data Sets with a Hierarchical Bayesian Score

10.30 – 11.00


11.00 – 12.30

Plenary presentations: Software demonstrations. Session chair: Jirka Vomlel

·      Rafael Cabañas, Alessandro Antonucci, David Huber, and Marco Zaffalon: CREDICI: A Java Library for Causal Inference by Credal Networks

·      Rafael Cabañas, Javier Cózar Antonio Salmerón, Andrés R. Masegosa: Probabilistic Graphical Models with Neural Networks in InferPy

·      Gaspard Ducamp, Christophe Gonzales, and Pierre-Henri Wuillemin: aGrUM/pyAgrum : a Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python

·      James Cussens: GOBNILP: Learning Bayesian network structure with integer programming

·      Anders L. Madsen, Kristian G. Olesen, Jørn Munkhof Møller, Nicolaj Søndberg-Jeppesen, Frank Jensen, Thomas Mulvad Jensen, Per Henriksen, Morten Lindblad, Trine Søby Christensen: A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital

·      Alex Markham, Aditya Chivukula, and Moritz Grosse-Wentrup: MeDIL: A Python Package for Causal Modelling

12.30 – 13.30


13.30 – 14.30

Poster presentations

14.30 – 16.00

Plenary presentations: Geographic theme. Session chair: Marco Valtorta

·      Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten and Ralf Möller: Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping

·      Cory Butz, Jhonatan Oliveira and Robert Peharz: Sum-Product Networ Decompilation

·      Denis Maua, Heitor Ribeiro, Gustavo Katague and Alessandro Antonucci: Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks

·      Karine Chubarian and Gyorgy Turan: Approximating bounded tree-width Bayesian network classifiers with OBDD

·      Cong Chen, Changhe Yuan and Chao Chen: Efficient Heuristic Search for M-Modes Inference

·      Cong Chen, Jiaqi Yang, Chao Chen and Changhe Yuan: Solving Multiple Inference by Minimizing Expected Loss

16.00 – 16.30


16.30 – 17.30

Poster presentations

17.30 – 18.30

Keynote presentation: Guy Van den Broeck


Conference dinner


Friday, September 25



09.00 – 10.30

Plenary presentations: Sum-product networks and Software demonstrations. Session chair: James Cussens

·      Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig and Kristian Kersting: Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

·      Fabrizio Ventola, Karl Stelzner, Alejandro Molina and Kristian Kersting: Residual Sum-Product Networks

·      Pierre Clavier, Olivier Bouaziz and Grégory Nuel: Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data

·      Tomas Pevny, Vasek Smidl, Martin Trapp, Ondrej Polacek and Tomas Oberhuber: Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations

·      Nikolas Bernaola Mario Michiels Concha Bielza Pedro Larrañaga: BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks

·      David Huber, Rafael Cabañas, Alessandro Antonucci, and Marco Zaffalon: CREMA: A Java Library for Credal Network Inference

10.30 – 11.00


11.00 – 12.30

Plenary presentations: Causality. Session chair: Milan Studený

·      David Kinney and David Watson: Causal Feature Learning for Utility-Maximizing Agents

·      Linda C. van der Gaag, Silja Renooij and Alessandro Facchini: Building Causal Interaction Models by Recursive Unfolding

·      Teny Handhayani and James Cussens: Kernel-based Approach for Learning Causal Graphs from Mixed Data

·      Kiattikun Chobtham and Anthony C. Constantinou: Bayesian network structure learning with causal effects in the presence of latent variables

·      Kari Rantanen, Antti Hyttinen and Matti Järvisalo: Learning Optimal Cyclic Causal Graphs from Interventional Data

·      Konrad P. Mielke, Tom Claassen, Mark A.J. Huijbregts, Aafke M. Schipper and Tom M. Heskes: Discovering cause-effect relationships in spatial systems with a known direction based on observational data

12.30 – 13.30


13.30 – 14.30

Plenary presentations: Theoretical foundations and Causality. Session chair: Cassio de Campos

·      Milan Studeny, James Cussens and Vaclav Kratochvil: Dual Formulation of the Chordal Graph Conjecture

·      Linda C. van der Gaag and Janneke Bolt: Poset Representations for Sets of Elementary Triplets

·      Konstantina Biza, Ioannis Tsamardinos and Sofia Triantafillou: Tuning Causal Discovery Algorithms

·      Marco Zaffalon, Alessandro Antonucci and Rafael Cabañas de Paz: Structural Causal Models Are (Solvable by) Credal Networks

14.30 – 15.00


15.00 – 16.30

Poster presentations


Community meeting