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PGM 2020: Aalborg, Denmark
- Manfred Jaeger, Thomas Dyhre Nielsen:
International Conference on Probabilistic Graphical Models, PGM 2020, 23-25 September 2020, Aalborg, Hotel Comwell Rebild Bakker, Skørping, Denmark. Proceedings of Machine Learning Research 138, PMLR 2020 - Thomas D. Nielsen, Manfred Jaeger:
Preface. 1-4 - Laura Azzimonti, Giorgio Corani, Marco Scutari:
Structure Learning from Related Data Sets with a Hierarchical Bayesian Score. 5-16 - Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou:
Tuning Causal Discovery Algorithms. 17-28 - Tjebbe Bodewes, Marco Scutari:
Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data. 29-40 - Alessandro Bregoli, Marco Scutari, Fabio Stella:
Constraing-Based Learning for Continous-Time Bayesian Networks. 41-52 - Cory J. Butz, Jhonatan de S. Oliveira, Robert Peharz:
Sum-Product Network Decompilation. 53-64 - Cong Chen, Jiaqi Yang, Chao Chen, Changhe Yuan:
Solving Multiple Inference by Minimizing Expected Loss. 65-76 - Cong Chen, Changhe Yuan, Chao Chen:
Efficient Heuristic Search for M-Modes Inference. 77-88 - Yizuo Chen, Arthur Choi, Adnan Darwiche:
Supervised Learning with Background Knowledge. 89-100 - Kiattikun Chobtham, Anthony C. Constantinou:
Bayesian network structure learning with causal effects in the presence of latent variables. 101-112 - Karine Chubarian, György Turán:
Approximating bounded tree-width Bayesian network classifiers with OBDD. 113-124 - Pierre Clavier, Olivier Bouaziz, Grégory Nuel:
Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data. 125-136 - Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: Learning Structured-Decomposable Probabilistic Circuits. 137-148 - Cassio P. de Campos:
Almost No News on the Complexity of MAP in Bayesian Networks. 149-160 - Fan Ding, Yexiang Xue:
Contrastive Divergence Learning with Chained Belief Propagation. 161-172 - Gaspard Ducamp, Philippe Bonnard, Anthony Nouy, Pierre-Henri Wuillemin:
An Efficient Low-Rank Tensors Representation for Algorithms in Complex Probabilistic Graphical Models. 173-184 - Evan Dufraisse, Philippe Leray, Raphaël Nedellec, Tarek Benkhelif:
Interactive Anomaly Detection in Mixed Tabular Data using Bayesian Networks. 185-196 - Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller:
Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping. 197-208 - Pierre Gillot, Pekka Parviainen:
Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph. 209-220 - Teny Handhayani, James Cussens
:
Kernel-based Approach for Learning Causal Graphs from Mixed Data. 221-232 - Mattis Hartwig, Ralf Möller:
Lifted Query Answering in Gaussian Bayesian Networks. 233-244 - Radim Jirousek:
On a possibility of gradual model-learning. 245-256 - David Kinney, David S. Watson:
Causal Feature Learning for Utility-Maximizing Agents. 257-268 - Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang:
Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). 269-280 - Anders L. Madsen, Kristian G. Olesen, Heidi Lynge Løvschall, Nicolaj Søndberg-Jeppesen, Frank Jensen, Morten Lindblad, Mads Lause Mogensen, Trine Søby Christensen:
Prediction of High Risk of Deviations in Home Care Deliveries. 281-292 - Denis Deratani Mauá, Heitor Ribeiro Reis, Gustavo Perez Katague, Alessandro Antonucci:
Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks. 293-304 - Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes:
Discovering cause-effect relationships in spatial systems with a known direction based on observational data. 305-316 - George Orfanides, Aritz Pérez:
Learning decomposable models by coarsening. 317-328 - Luis E. Ortiz, Boshen Wang, Ze Gong:
Correlated Equilibria for Approximate Variational Inference in MRFs. 329-340 - Tomás Pevný, Václav Smídl, Martin Trapp, Ondrej Polácek, Tomás Oberhuber:
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations. 341-352 - Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan:
Discriminative Non-Parametric Learning of Arithmetic Circuits. 353-364 - Kari Rantanen, Antti Hyttinen, Matti Järvisalo:
Learning Optimal Cyclic Causal Graphs from Interventional Data. 365-376 - Verónica Rodríguez-López
Knowledge Transfer for Learning Markov Equivalence Classes. 377-388 - Wolfgang Roth, Franz Pernkopf:
Differentiable TAN Structure Learning for Bayesian Network Classifiers. 389-400 - Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. 401-412 - Charupriya Sharma, Zhenyu A. Liao, James Cussens
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations. 413-424 - Yujia Shen, Arthur Choi, Adnan Darwiche:
A New Perspective on Learning Context-Specific Independence. 425-436 - Aditi Shenvi, Jim Q. Smith:
Constructing a Chain Event Graph from a Staged Tree. 437-448 - Milan Studený, James Cussens
Dual Formulation of the Chordal Graph Conjecture. 449-460 - Shouta Sugahara, Itsuki Aomi, Maomi Ueno:
Bayesian Network Model Averaging Classifiers by Subbagging. 461-472 - Topi Talvitie, Pekka Parviainen:
Learning Bayesian Networks with Cops and Robbers. 473-484 - Nazanin Khosravani Tehrani, Nimar S. Arora, Yucen Lily Li, Kinjal Divesh Shah, David Noursi, Michael Tingley, Narjes Torabi, Sepehr Masouleh, Eric Lippert, Erik Meijer:
Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference. 485-496 - Veronica Tozzo, Davide Garbarino, Annalisa Barla:
Missing Values in Multiple Joint Inference of Gaussian Graphical Models. 497-508 - Linda C. van der Gaag, Silja Renooij, Alessandro Facchini:
Building Causal Interaction Models by Recursive Unfolding. 509-520 - Linda C. van der Gaag, Janneke H. Bolt:
Poset Representations for Sets of Elementary Triplets. 521-532 - Jos van de Wolfshaar, Andrzej Pronobis:
Deep Generalized Convolutional Sum-Product Networks. 533-544 - Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting:
Residual Sum-Product Networks. 545-556 - Cen Wan, Alex Alves Freitas:
Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces. 557-568 - Xiufan Yu, Karthikeyan Shanmugam
Hawkesian Graphical Event Models. 569-580 - Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas:
Structural Causal Models Are (Solvable by) Credal Networks. 581-592 - Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga:
BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks. 593-596 - Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon:
CREDICI: A Java Library for Causal Inference by Credal Networks. 597-600 - Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa:
Probabilistic Graphical Models with Neural Networks in InferPy. 601-604 - James Cussens
GOBNILP: Learning Bayesian network structure with integer programming. 605-608 - Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin:
aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python. 609-612 - David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon:
CREMA: A Java Library for Credal Network Inference. 613-616 - Anders L. Madsen, Kristian G. Olesen, Jørn Munkhof Møller, Nicolaj Søndberg-Jeppesen, Frank Jensen, Thomas Mulvad Larsen, Per Henriksen, Morten Lindblad, Trine Søby Christensen:
A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital. 617-620 - Alex Markham, Aditya Chivukula, Moritz Grosse-Wentrup:
MeDIL: A Python Package for Causal Modelling. 621-624 - Jonathan Serrano-Pérez, Luis Enrique Sucar:
PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python. 625-628
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