{"id":153,"date":"2020-07-24T14:45:55","date_gmt":"2020-07-24T14:45:55","guid":{"rendered":"https:\/\/pgm2020.cs.aau.dk\/?page_id=153"},"modified":"2020-07-26T08:04:38","modified_gmt":"2020-07-26T08:04:38","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/pgm2020.cs.aau.dk\/index.php\/accepted-papers\/","title":{"rendered":"Accepted papers"},"content":{"rendered":"<div class=\"subcontent\">\n<div class=\"accepted\">\n<ul class=\"ili-indent\">\n<li class=\"paper\"><span class=\"authors\">Cen Wan and Alex A. Freitas. <\/span><span class=\"title\">Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">David Kinney and David Watson. <\/span><span class=\"title\">Causal Feature Learning for Utility-Maximizing Agents<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Ver\u00f3nica Rodr\u00edguez-L\u00f3pez and Luis Enrique Sucar. <\/span><span class=\"title\">Knowledge Transfer for Learning Markov Equivalence Classes<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Karine Chubarian and Gyorgy Turan. <\/span><span class=\"title\">Approximating bounded tree-width Bayesian network classifiers with OBDD<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Tjebbe Bodewes and Marco Scutari. <\/span><span class=\"title\">Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Perharz, Thomas Liebig and Kristian Kersting. <\/span><span class=\"title\">Conditional Sum-Product Networks: Composing Neural Networks into Probabilistic Tractable Models<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Radim Jirou\u0161ek. <\/span><span class=\"title\">On a Possibility of Gradual Model-Learning<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Veronica Tozzo, Davide Garbarino and Annalisa Barla. <\/span><span class=\"title\">Missing Values in Multiple Joint Inference of Gaussian Graphical Models<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten and Ralf M\u00f6ller. <\/span><span class=\"title\">Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Nazanin Tehrani, Nimar Arora, Yucen Li, Kinjal Shah, David Noursi, Michael Tingley, Narjes Torabi, Sepehr Masouleh, Eric Lippert and Erik Meijer. <\/span><span class=\"title\">Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Linda C. van der Gaag, Silja Renooij and Alessandro Facchini. <\/span><span class=\"title\">Building Causal Interaction Models by Recursive Unfolding<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Cory Butz, Jhonatan Oliveira and Robert Peharz. <\/span><span class=\"title\">Sum-Product Network Decompilation<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Mattis Hartwig and Ralf M\u00f6ller. <\/span><span class=\"title\">Lifted Query Answering in Gaussian Bayesian Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Evan Dufraisse, Philippe Leray, Rapha\u00ebl Nedellec and Tarek Benkhelif. <\/span><span class=\"title\">Anomaly Detection using Bayesian Network<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Milan Studeny, James Cussens and Vaclav Kratochvil. <\/span><span class=\"title\">Dual Formulation of the Chordal Graph Conjecture<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Fabrizio Ventola, Karl Stelzner, Alejandro Molina and Kristian Kersting. <\/span><span class=\"title\">Residual Sum-Product Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Pierre Clavier, Olivier Bouaziz and Gr\u00e9gory Nuel. <\/span><span class=\"title\">Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Alessandro Bregoli, Marco Scutari and Fabio Stella. <\/span><span class=\"title\">Constraint-Based Learning for Continuous-Time Bayesian Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Tomas Pevny, Vasek Smidl, Martin Trapp, Ondrej Polacek and Tomas Oberhuber. <\/span><span class=\"title\">Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">George Orfanides and Aritz P\u00e9rez. <\/span><span class=\"title\">Learning decomposable models by coarsening<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Kiattikun Chobtham and Anthony C. Constantinou. <\/span><span class=\"title\">Bayesian network structure learning with causal effects in the presence of latent variables<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Pierre Gillot and Pekka Parviainen. <\/span><span class=\"title\">Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Topi Talvitie and Pekka Parviainen. <\/span><span class=\"title\">Learning Bayesian Networks with Cops and Robbers<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Denis Maua, Heitor Ribeiro, Gustavo Katague and Alessandro Antonucci. <\/span><span class=\"title\">Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Charupriya Sharma, Zhenyu Liao, James Cussens and Peter van Beek. <\/span><span class=\"title\">A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Meihua Dang, Antonio Vergari and Guy Van den Broeck. <\/span><span class=\"title\">Strudel: Learning Structured-Decomposable Probabilistic Circuits<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian and Lingzhou Xue. <\/span><span class=\"title\">Hawkesian Graphical Event Models<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Fan Ding and Yexiang Xue. <\/span><span class=\"title\">Contrastive Divergence Learning with Chained Belief Propagation<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Jos van de Wolfshaar and Andrzej Pronobis. <\/span><span class=\"title\">Deep Generalized Convolutional Sum-Product Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Aditi Shenvi and Jim Q. Smith. <\/span><span class=\"title\">Constructing a Chain Event Graph from a Staged Tree<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Wolfgang Roth and Franz Pernkopf. <\/span><span class=\"title\">Differentiable TAN Structure Learning for Bayesian Network Classifiers<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Shouta Sugahara, Itsuki Aomi and Maomi Ueno. <\/span><span class=\"title\">Bayesian Network Model Averaging Classifiers by Bagging<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Kari Rantanen, Antti Hyttinen and Matti J\u00e4rvisalo. <\/span><span class=\"title\">Learning Optimal Cyclic Causal Graphs from Interventional Data<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Konrad P. Mielke, Tom Claassen, Mark A.J. Huijbregts, Aafke M. Schipper and Tom M. Heskes. <\/span><span class=\"title\">Discovering cause-effect relationships in spatial systems with a known direction based on observational data<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Ond\u0159ej Ku\u017eelka, Vyacheslav Kungurtsev and Yuyi Wang. <\/span><span class=\"title\">Lifted Weight Learning of Markov Logic Networks (Revisited Once More Time)<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Gaspard Ducamp, Philippe Bonnard, Anthony Nouy and Pierre-Henri Wuillemin. <\/span><span class=\"title\">An Efficient Low-Rank Tensors Representations for Algorithms in Complex Probabilistic Graphical Models<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Konstantina Biza, Ioannis Tsamardinos and Sofia Triantafillou. <\/span><span class=\"title\">Tuning Causal Discovery Algorithms<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Laura Azzimonti, Giorgio Corani and Marco Scutari. <\/span><span class=\"title\">Structure Learning from Related Data Sets with a Hierarchical Bayesian Score<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Marco Zaffalon, Alessandro Antonucci and Rafael Caba\u00f1as de Paz. <\/span><span class=\"title\">Structural Causal Models Are Credal Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Cong Chen, Jiaqi Yang, Chao Chen and Changhe Yuan. <\/span><span class=\"title\">Solving Multiple Inference by Minimizing Expected Loss<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Cong Chen, Changhe Yuan and Chao Chen. <\/span><span class=\"title\">Efficient Heuristic Search for M-Modes Inference<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Cassio P. de Campos. <\/span><span class=\"title\">Almost No News on the Complexity of MAP in Bayesian Networks<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Linda C. van der Gaag and Janneke Bolt. <\/span><span class=\"title\">Poset Representations for Sets of Elementary Triplets<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Nandini Ramanan, Mayukh Das, Kristian Kersting and Sriraam Natarajan. <\/span><span class=\"title\">Discriminative Non-Parametric Learning of Arithmetic Circuits<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Yizuo Chen, Arthur Choi and Adnan Darwiche. <\/span><span class=\"title\">Supervised Learning with Background Knowledge<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Luis Ortiz, Boshen Wang and Ze Gong. <\/span><span class=\"title\">Correlated Equilibria for Approximate Variational Inference in MRFs<\/span><\/li>\n<li class=\"paper\"><span class=\"authors\">Yujia Shen, Arthur Choi and Adnan Darwiche. <\/span><span class=\"title\">A New Perspective on Learning Context-Specific Independence<\/span><\/li>\n<\/ul>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Cen Wan and Alex A. Freitas. Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces David Kinney and David Watson. Causal Feature Learning for Utility-Maximizing Agents Ver\u00f3nica Rodr\u00edguez-L\u00f3pez and Luis Enrique Sucar. Knowledge Transfer for Learning Markov Equivalence Classes Karine Chubarian and Gyorgy Turan. Approximating bounded tree-width Bayesian network classifiers with OBDD Tjebbe &hellip; <a href=\"https:\/\/pgm2020.cs.aau.dk\/index.php\/accepted-papers\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Accepted papers&#8221;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-153","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/pages\/153","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/comments?post=153"}],"version-history":[{"count":3,"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/pages\/153\/revisions"}],"predecessor-version":[{"id":158,"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/pages\/153\/revisions\/158"}],"wp:attachment":[{"href":"https:\/\/pgm2020.cs.aau.dk\/index.php\/wp-json\/wp\/v2\/media?parent=153"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}