PGM 2020

58 papers

PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python

Jonathan Serrano-Pérez, L. Enrique Sucar

MeDIL: A Python Package for Causal Modelling

Alex Markham, Aditya Chivukula, Moritz Grosse-wentrup

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

CREMA: A Java Library for Credal Network Inference

David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon

GOBNILP: Learning Bayesian network structure with integer programming

James Cussens

Probabilistic Graphical Models with Neural Networks in InferPy

Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa

CREDICI: A Java Library for Causal Inference by Credal Networks

Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon

BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks

Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga

aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python

Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin

Structural Causal Models Are (Solvable by) Credal Networks

Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas

Hawkesian Graphical Event Models

Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian

Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces

Cen Wan, Alex Freitas

Residual Sum-Product Networks

Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting

Deep Generalized Convolutional Sum-Product Networks

Jos Wolfshaar, Andrzej Pronobis

Poset Representations for Sets of Elementary Triplets

L. C. van der Gaag, J. H. Bolt

Building Causal Interaction Models by Recursive Unfolding

L. C. van der Gaag, S. Renooij, A. Facchini

Missing Values in Multiple Joint Inference of Gaussian Graphical Models

Veronica Tozzo, Davide Garbarino, Annalisa Barla

Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference

Nazanin Tehrani, Nimar S. Arora, Yucen Lily Li, Kinjal Divesh Shah, David Noursi

Learning Bayesian Networks with Cops and Robbers

Topi Talvitie, Pekka Parviainen

Bayesian Network Model Averaging Classifiers by Subbagging

Shouta Sugahara, Itsuki Aomi, Maomi Ueno

Dual Formulation of the Chordal Graph Conjecture

Milan Studený, James Cussens, Václav Kratochvíl

Constructing a Chain Event Graph from a Staged Tree

Aditi Shenvi, Jim Q Smith

A New Perspective on Learning Context-Specific Independence

Yujia Shen, Arthur Choi, Adnan Darwiche

A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations

Charupriya Sharma, Zhenyu A. Liao, James Cussens, Peter Beek

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz

Differentiable TAN Structure Learning for Bayesian Network Classifiers

Wolfgang Roth, Franz Pernkopf

Knowledge Transfer for Learning Markov Equivalence Classes

Verónica Rodríguez-López, Luis Enrique Sucar

Learning Optimal Cyclic Causal Graphs from Interventional Data

Kari Rantanen, Antti Hyttinen, Matti Järvisalo

Discriminative Non-Parametric Learning of Arithmetic Circuits

Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan

Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations

Tomas Pevny, Vaclav Smidl, Martin Trapp, Ondřej Poláček, Tomáš Oberhuber

Luis E. Ortiz, Boshen Wang, Ze Gong

Learning decomposable models by coarsening

George Orfanides, Aritz Pérez

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

Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks

Denis Deratani Mauá, Heitor Ribeiro Reis, Gustavo Perez Katague, Alessandro Antonucci

Prediction of High Risk of Deviations in Home Care Deliveries

Anders L. Madsen, Kristian G. Olesen, Heidi Lynge Løvschall, Nicolaj Søndberg-Jeppesen, Frank Jensen

Lifted Weight Learning of Markov Logic Networks (Revisited One More Time)

Ondrej Kuzelka, Vyacheslav Kungurtsev, Yuyi Wang

Causal Feature Learning for Utility-Maximizing Agents

David Kinney, David Watson

On a possibility of gradual model-learning

Radim Jiroušek

Lifted Query Answering in Gaussian Bayesian Networks

Mattis Hartwig, Ralf Möller

Kernel-based Approach for Learning Causal Graphs from Mixed Data

Teny Handhayani, James Cussens

Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph

Pierre Gillot, Pekka Parviainen

Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping

Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten, Ralf Möller

Interactive Anomaly Detection in Mixed Tabular Data using Bayesian Networks

Evan Dufraisse, Philippe Leray, Raphaël Nedellec, Tarek Benkhelif

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

Contrastive Divergence Learning with Chained Belief Propagation

Ding Fan, Xue Yexiang

Almost No News on the Complexity of MAP in Bayesian Networks

Cassio P. de Campos

Strudel: Learning Structured-Decomposable Probabilistic Circuits

Meihua Dang, Antonio Vergari, Guy Broeck

Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data

Clavier Pierre, Bouaziz Olivier, Nuel Gregory

Approximating bounded tree-width Bayesian network classifiers with OBDD

Karine Chubarian, György Turán

Bayesian network structure learning with causal effects in the presence of latent variables

Kiattikun Chobtham, Anthony C. Constantinou

Supervised Learning with Background Knowledge

Yizuo Chen, Arthur Choi, Adnan Darwiche

Efficient Heuristic Search for M-Modes Inference

Cong Chen, Changhe Yuan, Chao Chen

Solving Multiple Inference by Minimizing Expected Loss

Cong Chen, Jiaqi Yang, Chao Chen, Changhe Yuan

Sum-Product Network Decompilation

Cory Butz, Jhonatan S. Oliveira, Robert Peharz

Constraing-Based Learning for Continous-Time Bayesian Networks

Alessandro Bregoli, Marco Scutari, Fabio Stella

Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data

Tjebbe Bodewes, Marco Scutari

Tuning Causal Discovery Algorithms

Konstantina Biza, Ioannis Tsamardinos, Sofia Triantafillou

Structure Learning from Related Data Sets with a Hierarchical Bayesian Score

Laura Azzimonti, Giorgio Corani, Marco Scutari