Volume 72: International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic

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Editors: Václav Kratochvíl, Milan Studený

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Contents:

Preface

Proceedings of the 9th International Conference on Probabilistic Graphical Models

Václav Kratochvíl, Milan Studený ; PMLR 72:i-iv

Accepted Papers

Bayesian Network Classifiers Under the Ensemble Perspective

Jacinto Arias, José A. Gámez, José M. Puerta ; PMLR 72:1-12

Causal Structure Learning via Temporal Markov Networks

Aubrey Barnard, David Page ; PMLR 72:13-24

An Order-based Algorithm for Learning Structure of Bayesian Networks

Shahab Behjati, Hamid Beigy ; PMLR 72:25-36

A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks

Ioan Gabriel Bucur, Tom Bussel, Tom Claassen, Tom Heskes ; PMLR 72:37-48

An Empirical Study of Methods for SPN Learning and Inference

Cory J. Butz, Jhonatan S. Oliveira, André E. Santos, André L. Teixeira, Pascal Poupart, Agastya Kalra ; PMLR 72:49-60

A partial orthogonalization method for simulating covariance and concentration graph matrices

Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga ; PMLR 72:61-72

Cascading Sum-Product Networks using Robustness

Diarmaid Conaty, Jesús Martínez Del Rincon, Cassio P. De Campos ; PMLR 72:73-84

Markov Random Field MAP as Set Partitioning

James Cussens ; PMLR 72:85-96

Parallel Probabilistic Inference by Weighted Model Counting

Giso H. Dal, Alfons W. Laarman, Peter J.F. Lucas ; PMLR 72:97-108

Parameterized hardness of active inference

Nils Donselaar ; PMLR 72:109-120

Structure Learning Under Missing Data

Alexander Gain, Ilya Shpitser ; PMLR 72:121-132

Structure Learning for Bayesian Networks over Labeled DAGs

Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander ; PMLR 72:133-144

Solving M-Modes in Loopy Graphs Using Tree Decompositions

Cong Chen, Changhe Yuan, Ze Ye, Chao Chen ; PMLR 72:145-156

On the Relative Expressiveness of Bayesian and Neural Networks

Arthur Choi, Adnan Darwiche ; PMLR 72:157-168

Instance-Specific Bayesian Network Structure Learning

Fattaneh Jabbari, Shyam Visweswaran, Gregory F. Cooper ; PMLR 72:169-180

Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks

Priyank Jaini, Amur Ghose, Pascal Poupart ; PMLR 72:181-192

Finding Minimal Separators in LWF Chain Graphs

Mohammad Ali Javidian, Marco Valtorta ; PMLR 72:193-200

A sum-product algorithm with polynomials for computing exact derivatives of the likelihood in Bayesian networks

Alexandra Lefebvre, Grégory Nuel ; PMLR 72:201-212

Learning Non-parametric Markov Networks with Mutual Information

Janne Leppä-Aho, Santeri Räisänen, Xiao Yang, Teemu Roos ; PMLR 72:213-224

Bayesian Network Structure Learning with Side Constraints

Andrew Li, Peter Beek ; PMLR 72:225-236

Making Continuous Time Bayesian Networks More Flexible

Manxia Liu, Fabio Stella, Arjen Hommersom, Peter J.F. Lucas ; PMLR 72:237-248

A Novel Approach to Handle Inference in Discrete Markov Networks with Large Label Sets

Alexander Oliver Mader, Jens Berg, Cristian Lorenz, Carsten Meyer ; PMLR 72:249-259

Simple Propagation with Arc-Reversal in Bayesian Networks

Anders Madsen, Cory J. Butz, Jhonatan S. Oliveira, André E. Santos ; PMLR 72:260-271

Learning Bayesian network classifiers with completed partially directed acyclic graphs

Bojan Mihaljević, Concha Bielza, Pedro Larrañaga ; PMLR 72:272-283

Consistent Estimation given Missing Data

Karthika Mohan, Judea Pearl ; PMLR 72:284-295

Intervals of Causal Effects for Learning Causal Graphical Models

Samuel Montero-Hernandez, Felipe Orihuela-Espina, Luis Enrique Sucar ; PMLR 72:296-307

Unifying DAGs and UGs

Jose M. Peña ; PMLR 72:308-319

Approximating the maximum weighted decomposable graph problem with applications to probabilistic graphical models

Aritz Pérez, Christian Blum, Jose A. Lozano ; PMLR 72:320-331

Sparse Learning in Gaussian Chain Graphs for State Space Models

Lasse Petersen ; PMLR 72:332-343

Learning Optimal Causal Graphs with Exact Search

Kari Rantanen, Antti Hyttinen, Matti Järvisalo ; PMLR 72:344-355

Discriminative Training of Sum-Product Networks by Extended Baum-Welch

Abdullah Rashwan, Pascal Poupart, Chen Zhitang ; PMLR 72:356-367

Same-Decision Probability: Threshold Robustness and Application to Explanation

Silja Renooij ; PMLR 72:368-379

Circular Chain Classifiers

Jesús Joel Rivas, Felipe Orihuela-Espina, Luis Enrique Succar ; PMLR 72:380-391

Discrete model-based clustering with overlapping subsets of attributes

Fernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza ; PMLR 72:392-403

Differential networking with path weights in Gaussian trees

Alberto Roverato, Robert Castelo ; PMLR 72:404-415

Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms?

Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez ; PMLR 72:416-427

Formal Verification of Bayesian Network Classifiers

Andy Shih, Arthur Choi, Adnan Darwiche ; PMLR 72:427-438

Exact learning augmented naive Bayes classifier

Shouta Sugahara, Masaki Uto, Maomi Ueno ; PMLR 72:439-450

Finding Optimal Bayesian Networks with Local Structure

Topi Talvitie, Ralf Eggeling, Mikko Koivisto ; PMLR 72:451-462

Representations of Bayesian networks by low-rank models

Petr Tichavský, Jiří Vomlel ; PMLR 72:463-474

Forward-Backward Splitting for Time-Varying Graphical Models

Federico Tomasi, Veronica Tozzo, Alessandro Verri, Saverio Salzo ; PMLR 72:475-486

A Lattice Representation of Independence Relations

Linda C. van der Gaag, Marco Baioletti, Janneke H. Bolt ; PMLR 72:487-498

Naive Bayesian Classifiers with Extreme Probability Features

Linda C. van der Gaag, Andrea Capotorti ; PMLR 72:499-510

Learning Bayesian Networks by Branching on Constraints

Thijs van Ommen ; PMLR 72:511-522

Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models

Yang Xiang, Abdulrahman Alshememry ; PMLR 72:523-534

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