PGM 2018

46 papers

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

Yang Xiang, Abdulrahman Alshememry

Learning Bayesian Networks by Branching on Constraints

Thijs van Ommen

Naive Bayesian Classifiers with Extreme Probability Features

Linda C. van der Gaag, Andrea Capotorti

A Lattice Representation of Independence Relations

Linda C. van der Gaag, Marco Baioletti, Janneke H. Bolt

Forward-Backward Splitting for Time-Varying Graphical Models

Federico Tomasi, Veronica Tozzo, Alessandro Verri, Saverio Salzo

Representations of Bayesian networks by low-rank models

Petr Tichavský, Jiří Vomlel

Finding Optimal Bayesian Networks with Local Structure

Topi Talvitie, Ralf Eggeling, Mikko Koivisto

Exact learning augmented naive Bayes classifier

Shouta Sugahara, Masaki Uto, Maomi Ueno

Formal Verification of Bayesian Network Classifiers

Andy Shih, Arthur Choi, Adnan Darwiche

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

Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez

Differential networking with path weights in Gaussian trees

Alberto Roverato, Robert Castelo

Discrete model-based clustering with overlapping subsets of attributes

Fernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza

Circular Chain Classifiers

Jesús Joel Rivas, Felipe Orihuela-Espina, Luis Enrique Succar

Same-Decision Probability: Threshold Robustness and Application to Explanation

Silja Renooij

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

Abdullah Rashwan, Pascal Poupart, Chen Zhitang

Learning Optimal Causal Graphs with Exact Search

Kari Rantanen, Antti Hyttinen, Matti Järvisalo

Sparse Learning in Gaussian Chain Graphs for State Space Models

Lasse Petersen

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

Aritz Pérez, Christian Blum, Jose A. Lozano

Unifying DAGs and UGs

Jose M. Peña

Intervals of Causal Effects for Learning Causal Graphical Models

Samuel Montero-Hernandez, Felipe Orihuela-Espina, Luis Enrique Sucar

Consistent Estimation given Missing Data

Karthika Mohan, Judea Pearl

Learning Bayesian network classifiers with completed partially directed acyclic graphs

Bojan Mihaljević, Concha Bielza, Pedro Larrañaga

Simple Propagation with Arc-Reversal in Bayesian Networks

Anders Madsen, Cory J. Butz, Jhonatan S. Oliveira, André E. Santos

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

Alexander Oliver Mader, Jens Berg, Cristian Lorenz, Carsten Meyer

Making Continuous Time Bayesian Networks More Flexible

Manxia Liu, Fabio Stella, Arjen Hommersom, Peter J.F. Lucas

Bayesian Network Structure Learning with Side Constraints

Andrew Li, Peter Beek

Learning Non-parametric Markov Networks with Mutual Information

Janne Leppä-aho, Santeri Räisänen, Xiao Yang, Teemu Roos

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

Alexandra Lefebvre, Grégory Nuel

Finding Minimal Separators in LWF Chain Graphs

Mohammad Ali Javidian, Marco Valtorta

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

Priyank Jaini, Amur Ghose, Pascal Poupart

Instance-Specific Bayesian Network Structure Learning

Fattaneh Jabbari, Shyam Visweswaran, Gregory F. Cooper

On the Relative Expressiveness of Bayesian and Neural Networks

Arthur Choi, Adnan Darwiche

Solving M-Modes in Loopy Graphs Using Tree Decompositions

Cong Chen, Changhe Yuan, Ze Ye, Chao Chen

Structure Learning for Bayesian Networks over Labeled DAGs

Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander

Structure Learning Under Missing Data

Alexander Gain, Ilya Shpitser

Parameterized hardness of active inference

Nils Donselaar

Parallel Probabilistic Inference by Weighted Model Counting

Giso H. Dal, Alfons W. Laarman, Peter J.F. Lucas

Markov Random Field MAP as Set Partitioning

James Cussens

Cascading Sum-Product Networks using Robustness

Diarmaid Conaty, Jesus Martinez del Rincon, Cassio P. de Campos

A partial orthogonalization method for simulating covariance and concentration graph matrices

Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga

An Empirical Study of Methods for SPN Learning and Inference

Cory J. Butz, Jhonatan S. Oliveira, André E. Santos, André L. Teixeira, Pascal Poupart

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

Ioan Gabriel Bucur, Tom Bussel, Tom Claassen, Tom Heskes

An Order-based Algorithm for Learning Structure of Bayesian Networks

Shahab Behjati, Hamid Beigy

Causal Structure Learning via Temporal Markov Networks

Aubrey Barnard, David Page

Bayesian Network Classifiers Under the Ensemble Perspective

Jacinto Arias, José A. Gámez, José M. Puerta

Proceedings of the 9th International Conference on Probabilistic Graphical Models

Václav Kratochvíl, Milan Studený