ML Anthology

PGM 2020

58 papers

A New Perspective on Learning Context-Specific Independence Yujia Shen, Arthur Choi, Adnan Darwiche
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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
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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
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aGrUM/pyAgrum : A Toolbox to Build Models and Algorithms for Probabilistic Graphical Models in Python Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin
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Almost No News on the Complexity of MAP in Bayesian Networks Cassio P. de Campos
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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
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Approximating Bounded Tree-Width Bayesian Network Classifiers with OBDD Karine Chubarian, György Turán
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Bayesian Network Model Averaging Classifiers by Subbagging Shouta Sugahara, Itsuki Aomi, Maomi Ueno
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Bayesian Network Structure Learning with Causal Effects in the Presence of Latent Variables Kiattikun Chobtham, Anthony C. Constantinou
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BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga
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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, Eric Lippert, Erik Meijer
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Building Causal Interaction Models by Recursive Unfolding L. C. van der Gaag, S. Renooij, A. Facchini
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Causal Feature Learning for Utility-Maximizing Agents David Kinney, David Watson
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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
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Constraing-Based Learning for Continous-Time Bayesian Networks Alessandro Bregoli, Marco Scutari, Fabio Stella
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Constructing a Chain Event Graph from a Staged Tree Aditi Shenvi, Jim Q Smith
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Contrastive Divergence Learning with Chained Belief Propagation Ding Fan, Xue Yexiang
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Correlated Equilibria for Approximate Variational Inference in MRFs Luis E. Ortiz, Boshen Wang, Ze Gong
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CREDICI: A Java Library for Causal Inference by Credal Networks Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon
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CREMA: A Java Library for Credal Network Inference David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon
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Deep Generalized Convolutional Sum-Product Networks Jos Wolfshaar, Andrzej Pronobis
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Differentiable TAN Structure Learning for Bayesian Network Classifiers Wolfgang Roth, Franz Pernkopf
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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
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Discriminative Non-Parametric Learning of Arithmetic Circuits Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan
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Dual Formulation of the Chordal Graph Conjecture Milan Studeny, James Cussens, Vaclav Kratochvil
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Efficient Heuristic Search for M-Modes Inference Cong Chen, Changhe Yuan, Chao Chen
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Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data Clavier Pierre, Bouaziz Olivier, Nuel Gregory
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GOBNILP: Learning Bayesian Network Structure with Integer Programming James Cussens
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Hawkesian Graphical Event Models Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Lingzhou Xue
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Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces Cen Wan, Alex Freitas
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Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data Tjebbe Bodewes, Marco Scutari
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Interactive Anomaly Detection in Mixed Tabular Data Using Bayesian Networks Evan Dufraisse, Philippe Leray, Raphaël Nedellec, Tarek Benkhelif
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Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller
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Kernel-Based Approach for Learning Causal Graphs from Mixed Data Teny Handhayani, James Cussens
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Knowledge Transfer for Learning Markov Equivalence Classes Verónica Rodríguez-López, Luis Enrique Sucar
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Learning Bayesian Networks with Cops and Robbers Topi Talvitie, Pekka Parviainen
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Learning Decomposable Models by Coarsening George Orfanides, Aritz Pérez
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Learning Optimal Cyclic Causal Graphs from Interventional Data Kari Rantanen, Antti Hyttinen, Matti Järvisalo
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Lifted Query Answering in Gaussian Bayesian Networks Mattis Hartwig, Ralf Möller
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Lifted Weight Learning of Markov Logic Networks (Revisited One More Time) Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang
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MeDIL: A Python Package for Causal Modelling Alex Markham, Aditya Chivukula, Moritz Grosse-Wentrup
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Missing Values in Multiple Joint Inference of Gaussian Graphical Models Veronica Tozzo, Davide Garbarino, Annalisa Barla
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On a Possibility of Gradual Model-Learning Radim Jiroušek
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PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python Jonathan Serrano-Pérez, L. Enrique Sucar
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Poset Representations for Sets of Elementary Triplets L. C. van der Gaag, J. H. Bolt
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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
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Probabilistic Graphical Models with Neural Networks in InferPy Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa
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Residual Sum-Product Networks Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting
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Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph Pierre Gillot, Pekka Parviainen
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Solving Multiple Inference by Minimizing Expected Loss Cong Chen, Jiaqi Yang, Chao Chen, Changhe Yuan
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Structural Causal Models Are (Solvable by) Credal Networks Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
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Structure Learning from Related Data Sets with a Hierarchical Bayesian Score Laura Azzimonti, Giorgio Corani, Marco Scutari
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Strudel: Learning Structured-Decomposable Probabilistic Circuits Meihua Dang, Antonio Vergari, Guy Broeck
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Sum-Product Network Decompilation Cory Butz, Jhonatan S. Oliveira, Robert Peharz
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Sum-Product-Transform Networks: Exploiting Symmetries Using Invertible Transformations Tomáš Pevný, Václav Smídl, Martin Trapp, Ondřej Poláček, Tomáš Oberhuber
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Supervised Learning with Background Knowledge Yizuo Chen, Arthur Choi, Adnan Darwiche
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Tuning Causal Discovery Algorithms Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou
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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
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