Thomas D. Nielsen

ACM Trans. Spatial Algorithms Syst., 2022

Relational Fusion Networks: Graph Convolutional Networks for Road Networks.

[BibT_eX]

[DOI]

IEEE Trans. Intell. Transp. Syst., 2022

Graph Neural Networks for Microbial Genome Recovery.

[BibT_eX]

[DOI]

CoRR, 2022

Inducing Gaussian Process Networks.

[BibT_eX]

[DOI]

Alessandro Tibo

CoRR, 2022

Metagenomic binning with assembly graph embeddings.

[BibT_eX]

[DOI]

Bioinform., 2022

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Probabilistic Graphical Models, 2022

2021

Do You Feel the Same? On the Robustness of Cued-Recall Debriefing for User Experience Evaluation.

[BibT_eX]

[DOI]

ACM Trans. Comput. Hum. Interact., 2021

Probabilistic Models with Deep Neural Networks.

[BibT_eX]

[DOI]

Entropy, 2021

SolveDB+: SQL-Based Prescriptive Analytics.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Extending Database Technology, 2021

Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds.

[BibT_eX]

[DOI]

Proceedings of the 7th IFAC Conference on Analysis and Design of Hybrid Systems, 2021

2020

MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions.

[BibT_eX]

[DOI]

Inmaculada Pérez-Bernabé

Ana D. Maldonado

Antonio Salmerón

R J., 2020

Analyzing concept drift: A case study in the financial sector.

[BibT_eX]

[DOI]

Intell. Data Anal., 2020

Preface.

[BibT_eX]

[DOI]

Manfred Jaeger

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

2019

Prescriptive analytics: a survey of emerging trends and technologies.

[BibT_eX]

[DOI]

VLDB J., 2019

AMIDST: A Java toolbox for scalable probabilistic machine learning.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2019

On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network.

[BibT_eX]

[DOI]

CoRR, 2019

Graph Convolutional Networks for Road Networks.

[BibT_eX]

[DOI]

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2019

2018

A Review of Inference Algorithms for Hybrid Bayesian Networks.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2018

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2018

Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices.

[BibT_eX]

[DOI]

Proceedings of the Ninth International Conference on Future Energy Systems, 2018

On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network.

[BibT_eX]

[DOI]

Kristian Torp

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

MAP inference in dynamic hybrid Bayesian networks.

[BibT_eX]

[DOI]

Prog. Artif. Intell., 2017

A parallel algorithm for Bayesian network structure learning from large data sets.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2017

Scaling up Bayesian variational inference using distributed computing clusters.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2017

Bayesian Models of Data Streams with Hierarchical Power Priors.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Learning deterministic probabilistic automata from a model checking perspective.

[BibT_eX]

[DOI]

Mach. Learn., 2016

Anytime Decision Making Based on Unconstrained Influence Diagrams.

[BibT_eX]

[DOI]

Manuel Luque

Int. J. Intell. Syst., 2016

A scalable pairwise class interaction framework for multidimensional classification.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2016

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

d-VMP: Distributed Variational Message Passing.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Financial Data Analysis with PGMs Using AMIDST.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs.

[BibT_eX]

[DOI]

Proceedings of the ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, 2016

2015

Conditional Density Approximations with Mixtures of Polynomials.

[BibT_eX]

[DOI]

Int. J. Intell. Syst., 2015

Scalable learning of probabilistic latent models for collaborative filtering.

[BibT_eX]

[DOI]

Decis. Support Syst., 2015

Dynamic Bayesian modeling for risk prediction in credit operations.

[BibT_eX]

[DOI]

Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence, 2015

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XIV, 2015

MPE Inference in Conditional Linear Gaussian Networks.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

Parallel Importance Sampling in Conditional Linear Gaussian Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2015

Parallelisation of the PC Algorithm.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2015

2014

A classification-based approach to monitoring the safety of dynamic systems.

[BibT_eX]

[DOI]

Shengtong Zhong

Reliab. Eng. Syst. Saf., 2014

Learning mixtures of truncated basis functions from data.

[BibT_eX]

[DOI]

Inmaculada Pérez-Bernabé

Antonio Salmerón

Int. J. Approx. Reason., 2014

Special Issue on PGM-2012.

[BibT_eX]

[DOI]

Andrés Cano

Manuel Gómez-Olmedo

Int. J. Approx. Reason., 2014

A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

A Pairwise Class Interaction Framework for Multilabel Classification.

[BibT_eX]

[DOI]

Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

Requirement Engineering for a Small Project with Pre-Specified Scope.

[BibT_eX]

[DOI]

Proceedings of the 27th Norsk Informatikkonferanse, 2014

2013

Probabilistic decision graphs for optimization under uncertainty.

[BibT_eX]

[DOI]

Ann. Oper. Res., 2013

Learning Mixtures of Polynomials of Conditional Densities from Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2013

2012

Mixtures of truncated basis functions.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2012

A latent model for collaborative filtering.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2012

Learning Markov Decision Processes for Model Checking

[BibT_eX]

[DOI]

Proceedings of the Proceedings Quantities in Formal Methods, 2012

Learning Markov Models for Stationary System Behaviors.

[BibT_eX]

[DOI]

Kim Guldstrand Larsen

Brian Nielsen

Proceedings of the NASA Formal Methods, 2012

Active Learning of Markov Decision Processes for System Verification.

[BibT_eX]

[DOI]

Yingke Chen

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

2011

Learning Probabilistic Automata for Model Checking.

[BibT_eX]

[DOI]

Proceedings of the Eighth International Conference on Quantitative Evaluation of Systems, 2011

2010

Parameter estimation and model selection for mixtures of truncated exponentials.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2010

Special Issue on PGM-2008.

[BibT_eX]

[DOI]

Manfred Jaeger

Int. J. Approx. Reason., 2010

Towards a More Expressive Model for Dynamic Classification.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, 2010

2009

Inference in hybrid Bayesian networks.

[BibT_eX]

[DOI]

Reliab. Eng. Syst. Saf., 2009

Latent classification models for binary data.

[BibT_eX]

[DOI]

Kristian S. Ahlmann-Ohlsen

Pattern Recognit., 2009

A comparison of two approaches for solving unconstrained influence diagrams.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2009

Maximum Likelihood Learning of Conditional MTE Distributions.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009

2008

Adapting Bayes network structures to non-stationary domains.

[BibT_eX]

[DOI]

Søren Holbech Nielsen

Int. J. Approx. Reason., 2008

2007

On-line alert systems for production plants: A conflict based approach.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2007

2006

Classification using Hierarchical Naïve Bayes models.

[BibT_eX]

[DOI]

Mach. Learn., 2006

Sequential influence diagrams: A unified asymmetry framework.

[BibT_eX]

[DOI]

Prakash P. Shenoy

Int. J. Approx. Reason., 2006

Dynamic decision making without expected utility: An operational approach.

[BibT_eX]

[DOI]

Jean-Yves Jaffray

Eur. J. Oper. Res., 2006

Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings.

[BibT_eX]

[DOI]

Jens A. Hansen

Henrik Schiøler

Proceedings of the 2006 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2006), International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC 2006), 29 November, 2006

A COTS framework for sensor fusion using dynamic bayesian networks in livestock production.

[BibT_eX]

Jens A. Hansen

Henrik Schiøler

Proceedings of the ISCA 19th International Conference on Computer Applications in Industry and Engineering, 2006

2005

Latent Classification Models.

[BibT_eX]

[DOI]

Mach. Learn., 2005

Special Issue on ECSQARU-2003: The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty: Message from the Guest Editors.

[BibT_eX]

[DOI]

Nevin Lianwen Zhang

Int. J. Approx. Reason., 2005

Alert Systems for Production Plants: A Methodology Based on Conflict Analysis.

[BibT_eX]

[DOI]

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005

2004

Latent variable discovery in classification models.

[BibT_eX]

[DOI]

Nevin Lianwen Zhang

Artif. Intell. Medicine, 2004

Learning a decision maker's utility function from (possibly) inconsistent behavior.

[BibT_eX]

[DOI]

Artif. Intell., 2004

2003

Sensitivity analysis in influence diagrams.

[BibT_eX]

[DOI]

IEEE Trans. Syst. Man Cybern. Part A, 2003

Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2003

Representing and Solving Asymmetric Decision Problems.

[BibT_eX]

[DOI]

Int. J. Inf. Technol. Decis. Mak., 2003

2002

Decomposition of influence diagrams.

[BibT_eX]

[DOI]

J. Appl. Non Class. Logics, 2002

2001

Cutting Influence Diagrams Down to the Core.

[BibT_eX]

Proceedings of the SCAI'01, 2001

Structural Learning in Object Oriented Domains.

[BibT_eX]

[DOI]

Olav Bangsø

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference, 2001

2000

Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example.

[BibT_eX]

[DOI]

Pierre-Henri Wuillemin

Uffe Kjærulff

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Representing and Solving Asymmetric Bayesian Decision Problems.

[BibT_eX]

[DOI]

Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

1999

Welldefined Decision Scenarios.

[BibT_eX]

[DOI]