Research reports by Helge Langseth


"Official" listing:
Papers stored in Cristin can be found here



2017
Andrés Masegosa, Antonio Salmerón, Darío Ramos-López Helge Langseth, and Thomas D. Nielsen (2017):
Bayesian models of data streams with Hierarchical Power Priors
The Thirty-fourth International Conference on Machine Learning
Proceedings of Machine Learning Research 70: 2334-2343, 2017.
(Open access)

Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, and Helge Langseth (2017):
Inter-Session Modeling for Session-Based Recommendation
The 2nd WS on Deep Learning for Recommender systems at RECSYS
(Open access)

Andrés Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, and Darío Ramos-López (2017):
Scaling up Bayesian variational inference using distributed computing clusters
International Journal of Approximate Reasoning 88: 435-451, 2017.
(Preprint, Paper)

Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, and Thomas D. Nielsen (2017):
A Parallel Algorithm for Bayesian Network Structure Learning from Large Data Sets.
Knowledge-Based Systems 117: 46-55, 2017.
(Open access)

Darío Ramos-López, Andrés Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Thomas D. Nielsen, Helge Langseth, and Anders L. Madsen (2017):
MAP inference in dynamic hybrid Bayesian networks.
Progress in Artificial Intelligence 6: 133-144, 2017.
(Open access)


2016
Andrés Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, and Anders L. Madsen (2016):
d-VMP: Distributed Variational Message Passing.
International Conference on Probabilistic Graphical Models
Journal of Machine Learning Research: Workshop and Conference Proceedings 52: 321-332, 2016
(Open access)

Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés Masegosa, Helge Langseth, and Anders L. Madsen (2016):
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach.
International Conference on Probabilistic Graphical Models
Journal of Machine Learning Research: Workshop and Conference Proceedings 52: 415-425, 2016
(Open access)

Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, and Andrés Masegosa (2016):
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs.
22nd European Conference on Artificial Intelligence
Frontiers in Artificial Intelligence and Applications, Volume 285, pp. 743-750. IOS Press.
(Open access)


2015
Hanen Borchani, Ana M. Martínez, Andrés Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen and Ramón Sáez (2015):
Dynamic Bayesian modeling for risk prediction in credit operations.
The 13th Scandinavian Conference on Artificial Intelligence
(Preprint, Paper)

Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, and Thomas D. Nielsen (2015):
Parallelization of the PC Algorithm.
The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA'15)
(Preprint, Paper)

Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, and Thomas D. Nielsen (2015):
Parallel Importance Sampling in Conditional Linear Gaussian Networks.
The XVI Conference of the Spanish Association for Artificial Intelligence (CAEPIA'15)
(Preprint, Paper)

Hanen Borchani, Ana M. Martínez, Andrés Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez (2015):
Modeling concept drift: A probabilistic graphical model based approach
The Fourteenth International Symposium on Intelligent Data Analysis (IDA'15)
(Preprint, Paper)

Øyvind Myklatun, Thorstein Thorrud, Hai Nguyen, Helge Langseth, and Anders Kofod-Petersen (2015):
Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data.
ACM Recsys challenge workshop (Recsys'15)
(Preprint, Paper)

Boye Annfelt Høverstad, Axel Tidemann, Helge Langseth and Pinar Öztürk (2015):
Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning.
IEEE Transactions on Smart Grid 6(4): 1904-1913, 2015.
(Preprint, Paper)

Helge Langseth and Thomas D. Nielsen (2015):
Scalable learning of probabilistic latent models for collaborative filtering.
Decision Support Systems 74: 1-11, 2015.
(Preprint, Paper)

Inmaculada Pérez-Bernabé, Antonio Salmerón and Helge Langseth (2015):
Learning Conditional Distributions using Mixtures of Truncated Basis Functions
The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQUARU'15)
Springer Lecture Notes in Artificial Intelligence Volume 9161/2015, pp. 397-406
(Preprint, Paper)

Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen and Thomas D. Nielsen (2015):
MPE inference in Conditional Linear Gaussian Networks
The 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQUARU'15)
(Preprint, Paper)


2014
Anders L. Madsen, Frank Jensen, Martin Karlsen, Antonio Salmerón, Helge Langseth, and Thomas D. Nielsen (2014):
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs
Presented at the Seventh European Workshop on Probabilistic Graphical Models (PGM 2014),
Lecture Notes in Computer Science Vol. 8754, pp 302-317.
(Paper)

Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, and Antonio Salmerón (2014):
Learning mixtures of truncated basis functions from data
International Journal of Approximate Reasoning 55(4): 940-956, 2014.
(Preprint, Paper)

Hai Thanh Nguyen, Thomas Almenningen, Martin Havig, Herman Schistad, Anders Kofod-Petersen, Helge Langseth, and Heri Ramampiaro (2014):
Learning to Rank for Personalized Fashion Recommender Systems via Implicit Feedback.
Presented at the Second International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2014),
Lecture Notes in Computer Science Vol. 8891, pp. 51-61.
(Paper, Dataset)

Shengtong Zhong, Helge Langseth and Thomas D. Nielsen (2014):
A classification-based approach to monitoring the safety of dynamic systems
Reliability Engineering and System Safety 121: 61-71, 2014.
(Preprint, Paper)


2013
Helge Langseth (2013):
Beating the bookie: A look at statistical models for prediction of football matches
Presented at the 12th Scandinavian AI conference, Aalborg, Denmark, November 20-22, 2013.
(Paper, Slides)

Helge Langseth, David Marquez, and Martin Neil (2013): Fast approximate inference in hybrid Bayesian networks using dynamic discretisation
Presented at the Fifth International Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC 2013), Lecture Notes in Computer Science, Volume 7930, pp. 225-234.
(Paper, Slides)

Boye Annfelt Høverstad, Axel Tidemann and Helge Langseth (2013): Effects of data cleansing on load prediction algorithms.
Presented at the IEEE Symposium Series on Computational Intelligence, ISBN 978-1-4673-6002-9, pp. 93-100.
(Paper)

Axel Tidemann, Boye Annfelt Høverstad, Helge Langseth, and Pinar Öztürk. Effects of scale on load prediction algorithms.
Presented at the International Conference on Electricity Distribution (CIRED)
(Paper)


2012
Helge Langseth and Thomas D. Nielsen (2012):
A latent model for collaborative filtering
International Journal of Approximate Reasoning 53: 447-466, 2012.
(Early version, Paper)

Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2012):
Mixtures of Truncated Basis Functions
International Journal of Approximate Reasoning 53: 212-227, 2012.
(Preprint, Paper, Matlab code)

Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón (2012):
Learning Mixtures of Truncated Basis Functions from Data
Presented at the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012),
DECSAI, University of Granada Publications, ISBN 978-84-15536-57-4, pp. 163-170
(Paper, Slides)

Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2012):
Inference in hybrid Bayesian networks with Mixtures of Truncated Basis Functions
Presented at the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012),
DECSAI, University of Granada Publications, ISBN 978-84-15536-57-4, pp. 171-178
(Paper)


2011
Tore Bruland, Agnar Aamodt and Helge Langseth (2011):
A Hybrid CBR and BN Architecture Refined through Data Analysis
In the proceedings of The 11th International Conference on Intelligent Systems Design and Applications (ISDA), Cordoba, Spain, November 22-24, 2011. IEEE conference proceedings, ISBN 978-1-4577-1675-1. Pages 906-913.

Tor Gunnar Houeland, Tore Bruland, Agnar Aamodt and Helge Langseth (2011):
Combining CBR and BN using metareasoning
In Frontiers in Artificial Intelligence and Applications, Volum 227: The Eleventh Scandinavian Conference on Artificial Intelligence, Trondheim, Norway, May 24-26, 2011. Pages 189-191.

Terje N. Lilegraven, Arnt C. Wolden, Anders Kofod-Petersen and Helge Langseth (2011):
A design for a tourist CF system
In Frontiers in Artificial Intelligence and Applications, Volum 227: The Eleventh Scandinavian Conference on Artificial Intelligence, Trondheim, Norway, May 24-26, 2011. Pages 193-194.


2010
Anders Kofod-Petersen and Helge Langseth (2010):
Tourist Without a Cause.
Presented at the Second Norwegian Artificial Intelligence Symposium, Gjøvik, Norway, November 22, 2010.

Tore Bruland, Agnar Aamodt, and Helge Langseth (2010):
Architectures case-based reasoning and Bayesian networks for clinical decision support.
Proceedings of IIP2010 - 6th International Conference on Intelligent Information Processing. IFIP Conference Series. Manchester, UK, October 13-16, 2010. Springer Verlag, 2010.

Antonio Fernández, Helge Langseth, Thomas D. Nielsen, and Antonio Salmerón. Parameter learning in MTE networks using incomplete data.
Presented at the Fifth European Workshop on Probabilistic Graphical Models (PGM 2010), HIIT Publications 2010-2, pp. 137-145
(Paper)

Anders Kofod-Petersen, Helge Langseth, and Agnar Aamodt (2010):
Explanations in Bayesian networks using provenance trough case-based reasoning.
Presented at the 18th International Conference on Case-Based Reasoning (ICCBR 2010), Workshop Provenanace-Aware Case-Based Reasoning - Application to Reasoning, Metareasoning, Maintenance and Explanation, pp. 79 - 86, 2010. IEEE Computer Society.
(Preprint)

Shengtong Zhong, Ana M. Martinez, Thomas D. Nielsen, and Helge Langseth (2010):
Towards a More ExpressiveModel for Dynamic Classification
Presented at the 23rd Florida Artificial Intelligence Research Society Conference, AAAI, Florida, USA.
(Preprint)

Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2010):
Parameter Estimation for Mixtures of Truncated Exponentials
International Journal of Approximate Reasoning 51: 485-498, 2010.
(Preprint, Paper)


2009
Helge Langseth and Thomas D. Nielsen (2009):
A latent model for collaborative filtering
Technical Report 09-003, Department of Computer Science, Aalborg University
(Technical report, Journal version)

Shengtong Zhong and Helge Langseth (2009):
Local-Global-Learning of Naive Bayesian Classifier
Presented at ICICIC'09.
Proceedings of the Fourth International Conference on Innovative Computing, Information and Control, IEEE Computing Society, pp. 278-281.
(Preprint, Paper)

Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2009):
Maximum Likelihood Learning of Conditional MTE Distributions
Presented at ECSQARU 2009.
Springer Lecture Notes in Computer Science Volume 5590/2009, pp. 240-251
(Preprint, Paper)

Helge Langseth and Thomas D. Nielsen (2009):
Latent Classification models for Binary data
Pattern Recognition 42:2724-2736, 2009.
(Preprint, Paper, More information)

Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2009):
Inference in Hybrid Bayesian Networks
Reliability Engineering and System Safety 94:1499-1509, 2009.
(Preprint, Paper)


2008
Helge Langseth (2008):
Bayesian networks in Reliability: The Good, The Bad, and The Ugly.
Advances in Mathematical Modeling for Reliability. IOS Press 2008 ISBN 978-1-58603-865-6.
(Latest draft, Slides)

Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2008):
Parameter Estimation in Mixtures of Truncated Exponentials
Presented at PGM 08. (An extended version of the paper is published in IJAR.)
(Paper, Slides)


2007
Helge Langseth, Thomas D. Nielsen, Rafael Rumí, and Antonio Salmerón (2007):
Maximum Likelihood vs. Least Squares for Estimating Mixtures of Truncated Exponentials
Presented by Antonio Salmerón at INFORMS, 2007.
(Antonio's slides)

Helge Langseth (2007):
Bayesian networks in Reliability: The Good, The Bad, and The Ugly.
Presented at The fifth International Conference on Mathematical Models in Reliability, MMR'07.
(This was a keynote speech, so the slides are nice, but the paper was more of a rush-job. An extended version of the paper was published later.)

Helge Langseth and Finn V. Jensen (2007):
Bayesian belief network computation for reliability.
In Encyclopedia of Statistics in Quality and Reliability (Wiley)
(Preprint, Paper)

Helge Langseth and Luigi Portinale (2007):
Bayesian Networks in Reliability
Reliability Engineering and System Safety 92(1), pp. 92-108, 2007.
Early version available as Technical Report No. TR-INF-2005-04-01-UNIPMN, Dept. of Computer Science, University of Eastern Piedmont "Amedeo Avogadro", Alessandria, Italy.
(Preprint, Paper)

Helge Langseth and Luigi Portinale (2007):
Applications of Bayesian Networks in Reliability Analysis
In Ankush Mittal, Ashraf Kassim, Tele Tan (Eds.): Bayesian Network Technologies: Applications and Graphical Models (Idea Group Publishers, USA).
(Latest draft)


2006
Helge Langseth and Thomas D. Nielsen (2006):
Classification using Hierarchical Naive Bayes models
Machine Learning 63(2), pp. 135-159, 2006.
Early version available as Technical Report No. R-02-004, Dept. of Computer Science, Aalborg University.
(Preprint, Paper, Slides)

Helge Langseth and Bo Henry Lindqvist (2006):
Competing risks for repairable systems: A data study.
Journal of Statistical Planning and Inference 136(5), pp. 1683-1700, 2006.
(Preprint, Paper)

Bo Henry Lindqvist, Bård Støve and Helge Langseth (2006):
Modelling of dependence between critical failure and preventive maintenance: The repair alert model.
Journal of Statistical Planning and Inference 136(5), pp. 1701-1717, 2006.
(Preprint, Paper)

Jørn Vatn and Helge Langseth (2006):
Estimation of Weibull parameters when the i.i.d. assumption does not hold
In Lars Pettersson and Kaisa Simola (Eds.): Ageing of Components and Systems, pp. 128-141 (European Safety, Reliability & Data Association, Høvik, Norway).


2005
Helge Langseth and Thomas D. Nielsen (2005):
Latent Classification Models
Machine Learning 59(3), pp. 237-265, 2005.
Early version available as Technical Report No. R-04-020, Dept. of Computer Science, Aalborg University.
(Preprint, Paper, Slides)

Bo Henry Lindqvist and Helge Langseth (2005):
Statistical Modelling and Inference for Component Failure Times under Preventive Maintenance and Independent Censoring.
In: Modern Statistical and Mathematical Methods in Reliability, pp 319-333 (World Scientific Publishing, Singapore).
(Latest draft)

Per Hokstad, Helge Langseth, Bo Henry Lindqvist and Jørn Vatn (2005):
Failure-Model and Maintenance Optimization for a Railway Line
International Journal of Performability Engineering 1(1), pp. 51-64, 2005.
(Preprint)


2004
Helge Langseth (2004):
Data Mining: My view
A really simplistic introduction to Data Mining for undergraduate computer science students.

Helge Langseth (2004):
Bayesian Networks in Reliability: Some recent developments
Presented at The fourth International Conference on Mathematical Models in Reliability, MMR'04.
(Paper, Slides)

Thor Bjørkvoll and Helge Langseth (2004):
The Prioritization of Risk Reducing Measures in View of Uncertain Cost/Benefits
Presented at The Seventh international conference on probabilistic safety assessment and management, PSAM 7.
(Paper)


2003
Helge Langseth and Thomas D. Nielsen (2003):
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
Journal of Machine Learning Research 4, pp. 339-368.
(Paper)

Helge Langseth and Bo Henry Lindqvist (2003):
Competing risk combined with Imperfect repair: Some of the dirty details
Presented at the Workshop for Competing Risk
(Slides)

Helge Langseth and Bo Lindqvist (2003):
A maintenance model for components exposed to several failure mechanisms and imperfect repair.
In: Mathematical and Statistical Methods in Reliability, pp 415-430 (World Scientific Publishing, Singapore).
(Latest draft)

Helge Langseth and Finn V. Jensen (2003):
Decision Theoretic Troubleshooting of Coherent Systems.
Reliability Engineering and System Safety 80(1), pp. 49-61, 2003.
(Preprint, Paper)


2002
Helge Langseth (2002):
Bayesian networks with applications in reliability analysis
PhD thesis.
(Description, Single file)

Helge Langseth and Bo Henry Lindqvist (2002):
Modelling imperfect maintenance and repair of components under competing risk
Presented at The Third International Conference on Mathematical Models in Reliability, MMR'02.
(Paper, Slides)


2001
Olav Bangsø, Helge Langseth and Thomas D. Nielsen (2001):
Structural Learning in Object Oriented Domains.
Presented at The Fourteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS'01.
(Paper, Slides)

Helge Langseth and Finn Verner Jensen (2001):
Heuristics for two extensions of basic troubleshooting.
Presented at The Seventh Scandinavian Conference on Artificial Intelligence, SCAI'01.
(Paper, Slides)

Helge Langseth and Olav Bangsø (2001):
Parameter Learning in Object Oriented Bayesian Networks.
Annals of Mathematics and Artificial Intelligence 32(1/4), pp. 221-243, 2001.
(Preprint, Paper)

Finn V. Jensen, Uffe Kjærulff, Brian Krisitiansen, Helge Langseth, Claus Scanning, Marta Vomlelova and Jiri Vomlel (2001):
The SACSO methodology for troubleshooting complex systems.
Artificial Intelligence for Engineering, Design, Analysis and Manufacturing 15(4), pp. 321-333, 2001.
(Preprint, Paper)


Before 2000
Helge Langseth, Agnar Aamodt and Ole Martin Winnem (1999):
Learning Retrieval Knowledge from Data.
Presented at The IJCAI Workshop on Automating the Construction of Case Based Reasoners.
(Paper, Slides)

Helge Langseth (1999):
Modelling maintenance for components under competing risk.
Presented at The European Safety and Reliability Conference (ESREL) 1999.

Agnar Aamodt and Helge Langseth (1998):
Integrating Bayesian Networks into Knowledge-Intensive CBR.
Presented at The AAAI Workshop on Case-Based Reasoning Integrations.
(Paper)

Helge Langseth (1998):
Analysis of survival times using Bayesian networks (Slides).
Presented at The European Safety and Reliability Conference (ESREL) 1998.

Helge Langseth, Knut Haugen and Helge Sandtorv (1998):
Analysis of OREDA Data for Maintenance Optimisation.
Reliability Engineering and System Safety 60(2), pp. 103-110, 1998.
(Preprint, Paper)

Helge Langseth and Bo Henry Lindqvist (1998):
Uncertainty Bounds for a Monotone Multistate System.
Probability in the Engineering and Informational Sciences 12(2), pp. 239-260, 1998.
(Preprint, Paper)