Photo of Thomas Dyhre Nielsen
  • Selma Lagerløfs Vej 300, 1-2-34

    9220 Aalborg Ø

    Denmark

  • Selma Lagerlöfs Vej 300, Cassiopeia

    9220 Aalborg

    Denmark

19972019
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Research Output 1999 2019

2019
1 Citation (Scopus)

AMIDST: A Java toolbox for scalable probabilistic machine learning

Masegosa, A., Martinez, A. M., Ramos-López, D., Cabanas de Paz, R., Salmerón, A., Langseth, H., Nielsen, T. D. & Madsen, A. L., 1 Jan 2019, In : Knowledge-Based Systems. 163, p. 595-597 3 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Learning systems
Message passing
Probability distributions
Java
Machine learning

Prescriptive Analytics: A Survey of Emerging Trends And Technologies

Frazzetto, D., Nielsen, T. D., Pedersen, T. B. & Siksnys, L., 26 May 2019, In : V L D B Journal. 24 p.

Research output: Contribution to journalJournal articleResearchpeer-review

2018
2 Citations (Scopus)

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

Frazzetto, D., Neupane, B., Pedersen, T. B. & Nielsen, T. D., 12 Jun 2018, e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems. Association for Computing Machinery, p. 1-11 11 p.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Quality of service
Scheduling
Energy utilization
Uncertainty
70 Downloads (Pure)

A Review of Inference Algorithms for Hybrid Bayesian Networks

Salmerón, A., Rumí, R., Langseth, H., Nielsen, T. D. & Madsen, A. L., 1 Aug 2018, In : Journal of Artificial Intelligence Research. 62, p. 799-828 30 p.

Research output: Contribution to journalReview articleResearchpeer-review

Open Access
File
Bayesian networks
Costs

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

Jepsen, T. S., Jensen, C. S., Nielsen, T. D. & Torp, K., 2018, Proceedings of the 2018 IEEE International Conference on Big Data. IEEE, p. 3421-3430 10 p.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Learning systems
Availability
1 Citation (Scopus)

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks

Ramos-López, D., Masegosa, A., Salmerón, A., Rumí, R., Langseth, H., Nielsen, T. D. & Madsen, A. L., 1 Sep 2018, In : International Journal of Approximate Reasoning. 100, p. 115-134 20 p., 100.

Research output: Contribution to journalJournal articleResearchpeer-review

Importance sampling
Gaussian Mixture
Importance Sampling
Bayesian networks
Bayesian Networks
2017
17 Citations (Scopus)
44 Downloads (Pure)

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

Madsen, A. L., Jensen, F., Salmerón, A., Langseth, H. & Nielsen, T. D., 2017, In : Knowledge-Based Systems. 117, p. 46-55

Research output: Contribution to journalJournal articleResearchpeer-review

Open Access
File

Bayesian models of data streams with Hierarchical Power Priors

Masegosa, A., Nielsen, T. D., Langseth, H., Ramos-López, D., Salmerón, A. & Madsen, A. L., 2017, Proceedings of the 34th International Conference on Machine Learning. Vol. 70. p. 2334-2343

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Open Access
Computational efficiency
3 Citations (Scopus)

Financial Data Analysis with PGMs Using AMIDST

Cabanas, R., Martinez, A. M., Masegosa, A. R., Ramos-Lopez, D., Sameron, A., Nielsen, T. D., Langseth, H. & Madsen, A. L., 30 Jan 2017, Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016. IEEE, p. 1284-1287 4 p. 7836816

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Economics
2 Citations (Scopus)
44 Downloads (Pure)

MAP inference in dynamic hybrid Bayesian networks

Ramos-López, D., Masegosa, A., Martinez, A. M., Salmerón, A., Nielsen, T. D., Langseth, H. & Madsen, A. L., 2017, In : Progress in Artificial Intelligence. 6, 2, p. 133–144 12 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Open Access
File
6 Citations (Scopus)

Scaling up Bayesian variational inference using distributed computing clusters

Masegosa, A. R., Martinez, A. M., Langseth, H., Nielsen, T. D., Salmerón, A., Ramos-López, D. & Madsen, A. L., 1 Sep 2017, In : International Journal of Approximate Reasoning. 88, p. 435-451 17 p.

Research output: Contribution to journalJournal articleResearchpeer-review

Distributed computer systems
Distributed Computing
Variational Methods
Variational Bayes
Scaling
2016
55 Downloads (Pure)

A Java Toolbox for Analysis of MassIve Data STreams using Probabilistic Graphical Models

Masegosa, A., Martinez, A. M., Ramos-López, D., Langseth, H., Nielsen, T. D., Salmerón, A., Cabanas, R. & Madsen, A. L., 2016.

Research output: Contribution to conference without publisher/journalPosterResearchpeer-review

File
1 Citation (Scopus)
205 Downloads (Pure)

Anytime decision making based on unconstrained influence diagrams

Luque, M., Nielsen, T. D. & Jensen, F. V., 2016, In : International Journal of Intelligent Systems. 31, 4, p. 379-398 20 p.

Research output: Contribution to journalJournal articleResearchpeer-review

File
Influence Diagrams
Decision making
Decision Making
Optimal Policy
Temporal Constraints
4 Citations (Scopus)

A scalable pairwise class interaction framework for multidimensional classification

Arias, J., Gámez, J. A., Nielsen, T. D. & Puerta, J. M., 2016, In : International Journal of Approximate Reasoning. 68, p. 194–210

Research output: Contribution to journalJournal articleResearchpeer-review

Pairwise
Classifiers
Interaction
Scalability
Classifier
15 Citations (Scopus)

Bayesian Graphical Models

Jensen, F. V. & Nielsen, T. D., 2016, Wiley StatsRef: Statistics Reference Online. Wiley, p. 1-9 (Wiley StatsRef: Statistics Reference Online).

Research output: Contribution to book/anthology/report/conference proceedingEncyclopedia chapterResearchpeer-review

Bayesian networks
Probability distributions
Learning algorithms
Availability

d-VMP: Distributed Variational Message Passing

Masegosa, A., Martinez, A. M., Langseth, H., Nielsen, T. D., Salmerón, A., Ramos-López, D. & Madsen, A. L., 2016, JMLR Workshop and Conference Proceedings: Volume 52: Proceedings of the Eighth International Conference on Probabilistic Graphical Models. p. 321-332 12 p. (JMLR Workshop and Conference Proceedings, Vol. 52).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Open Access
11 Citations (Scopus)
178 Downloads (Pure)

Learning deterministic probabilistic automata from a model checking perspective

Mao, H., Chen, Y., Jaeger, M., Nielsen, T. D., Larsen, K. G. & Nielsen, B., 2016, In : Machine Learning. 105, 2, p. 255-299 45 p.

Research output: Contribution to journalJournal articleResearchpeer-review

File
Model checking
Learning algorithms
Temporal logic
Hardware

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

Salmerón, A., Madsen, A. L., Jensen, F., Langseth, H., Nielsen, T. D., Ramos-López, D., Martinez, A. M. & Masegosa, A., 2016, ECAI 2016: 22nd European Conference on Artificial Intelligence. IOS Press, p. 743-750 8 p. (Frontiers in Artificial Intelligence and Applications, Vol. 285).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Open Access

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

Ramos-López, D., Salmerón, A., Rumí, R., Martinez, A. M., Nielsen, T. D., Masegosa, A., Langseth, H. & Madsen, A. L., 2016, JMLR Workshop and Conference Proceedings: Volume 52: Proceedings of the Eighth International Conference on Probabilistic Graphical Models. p. 415-425 12 p. (JMLR Workshop and Conference Proceedings, Vol. 52).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Open Access
2015
307 Downloads (Pure)

AMIDST: Analysis of MassIve Data STreams

Masegosa, A., Martinez, A. M., Borchani, H., Ramos-López, D., Nielsen, T. D., Langseth, H., Salmerón, A. & Madsen, A. L., Oct 2015, The 27th Benelux Conference on Artificial Intelligence (BNAIC 2015).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Bayesian networks
Parallel algorithms
2 Citations (Scopus)
75 Downloads (Pure)

Conditional Density Approximations with Mixtures of Polynomials

Varando, G., López-Cruz, P. L., Nielsen, T. D., Bielza, C. & Larrañga, P., 2015, In : International Journal of Intelligent Systems. 30, 3, p. 236-264

Research output: Contribution to journalJournal articleResearchpeer-review

File
Conditional Density
Polynomials
Polynomial
Approximation
Basis Functions
4 Citations (Scopus)
251 Downloads (Pure)

Dynamic Bayesian modeling for risk prediction in credit operations

Borchani, H., Martinez, A. M., Masegosa, A., Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L. & Sáez, R., 2015, The 13th Scandinavian Conference on Artificial Intelligence (SCAI'2015). IOS Press, p. 17-26 (Frontiers in Artificial Intelligence and Applications, Vol. 278).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Feature extraction
Labels
Classifiers
7 Citations (Scopus)
328 Downloads (Pure)

Modeling concept drift: A probabilistic graphical model based approach

Borchani, H., Martinez, A. M., Masegosa, A. R., Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L. & Sáez, R., 2015, Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. Fromont, E., De Bie, T. & van Leeuwen, M. (eds.). Springer, p. 72-83 (Lecture Notes in Computer Science; No. 9385).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
196 Downloads (Pure)

MPE inference in conditional linear gaussian networks

Salmerón, A., Rumí, R., Langseth, H., Madsen, A. L. & Nielsen, T. D., 2015, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings. Destercke, S. & Denoeux, T. (eds.). Springer, p. 407-416 (Lecture Notes in Computer Science; No. 9161).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Bayesian networks
Computational complexity
6 Citations (Scopus)
211 Downloads (Pure)

Parallel importance sampling in conditional linear Gaussian networks

Salmerón, A., Ramos-López, D., Borchani, H., Martinez, A. M., Masegosa, A. R., Fernández, A., Langseth, H., Madsen, A. L. & Nielsen, T. D., 2015, Advances in Artificial Intelligence: 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015 Albacete, Spain, November 9–12, 2015 Proceedings. Puerta, J. M., Gámez, J. A., Dorronsoro, B., Barrenechea, E., Troncoso, A., Baruque, B. & Galar, M. (eds.). Springer, p. 36-46 (Lecture Notes in Computer Science; No. 9422).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Importance sampling
4 Citations (Scopus)
118 Downloads (Pure)

Parallelisation of the PC Algorithm

Madsen, A. L., Jensen, F., Salmerón, A., Langseth, H. & Nielsen, T. D., 2015, Advances in Artificial Intelligence: 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015 Albacete, Spain, November 9–12, 2015 Proceedings. Springer, p. 14-24 11 p. (Lecture Notes in Computer Science, Vol. 9422).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Bayesian networks
Parallel algorithms
Testing
Experiments
8 Citations (Scopus)
221 Downloads (Pure)

Scalable learning of probabilistic latent models for collaborative filtering

Langseth, H. & Nielsen, T. D., 2015, In : Decision Support Systems. 74

Research output: Contribution to journalJournal articleResearchpeer-review

File
Collaborative filtering
Statistical Models
Learning
Self-Help Groups
Scalability
2014
4 Citations (Scopus)
881 Downloads (Pure)

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

Zhong, S., Langseth, H. & Nielsen, T. D., Jan 2014, In : Reliability Engineering & System Safety. 121, p. 61-71

Research output: Contribution to journalJournal articleResearchpeer-review

File
Dynamical systems
Monitoring
Sensors
Experiments
8 Citations (Scopus)

A new method for vertical parallelisation of TAN learning based on balanced incomplete block designs

Madsen, A. L., Jensen, F., Salmerón, A., Karlsen, M., Langseth, H. & Nielsen, T. D., 2014, Proceedings of the 7th European Workshop on Probabilistic Graphical Models. Springer Publishing Company, Vol. 8754. p. 302-317 16 p. (Lecture Notes in Computer Science).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)

A pairwise class interaction framework for multilabel classification

Arias, J., Gámez, J. A., Nielsen, T. D. & Puerta, J. M., 2014, Proceedings of the 7th European Workshop on Probabilistic Graphical Models. van der Gaag, L. C. & Feelders, A. J. (eds.). Springer Publishing Company, Vol. 8754. p. 17-32 16 p. (Lecture Notes in Computer Science).

Research output: Contribution to book/anthology/report/conference proceedingConference abstract in proceedingResearchpeer-review

18 Citations (Scopus)
294 Downloads (Pure)

Learning Mixtures of Truncated Basis Functions from Data

Langseth, H., Nielsen, T. D., Pérez-Bernabé, I. & Salmerón, A., 2014, In : International Journal of Approximate Reasoning. 55, 4, p. 940-966

Research output: Contribution to journalJournal articleResearchpeer-review

File
Basis Functions
Bandwidth
Convex optimization
Bayesian networks
Distribution functions
854 Downloads (Pure)

Requirement Engineering for a Small Project with Pre-Specified Scope

Nielsen, T. D., Hovda, S., Antontio, F., Langseth, H., Madsen, A. L., Masegosa, A. & Salmerón, A., 2014, NIK: Norsk Informatikkonferanse . 12 p.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
2013
1 Citation (Scopus)
203 Downloads (Pure)

Learning Mixtures of Polynomials of Conditional Densities from Data

L. López-Cruz, P., Nielsen, T. D., Bielza, C. & Larrañga, P., 2013, Advances in Artificial Intelligence: 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013. Proceedings. Bielza et al., C. (ed.). Springer Publishing Company, p. 363-372 10 p. (Lecture Notes in Artificial Intelligence : Subseries of Lecture Notes in Computer Science; No. XVIII, Vol. 8109).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Conditional Density
Bayesian Networks
Polynomial
Approximation
Nonparametric Density Estimation
3 Citations (Scopus)
431 Downloads (Pure)

Probabilistic decision graphs for optimization under uncertainty

Jensen, F. V. & Nielsen, T. D., Apr 2013, In : Annals of Operations Research. 204, 1, p. 223-248

Research output: Contribution to journalJournal articleResearchpeer-review

File
Uncertainty
Graph
Influence diagrams
Modeling
Language
2012
10 Citations (Scopus)
333 Downloads (Pure)

Active Learning of Markov Decision Processes for System Verification

Chen, Y. & Nielsen, T. D., 12 Dec 2012, International Conference on Machine Learning and Applications (ICMLA). p. 289-294

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Learning systems
Problem-Based Learning
26 Citations (Scopus)
840 Downloads (Pure)

A latent model for collaborative filtering

Langseth, H. & Nielsen, T. D., Jun 2012, In : International Journal of Approximate Reasoning. 53, 4, p. 447–466 20 p.

Research output: Contribution to journalJournal articleResearchpeer-review

File
Collaborative filtering
Collaborative Filtering
Recommender systems
Recommender Systems
Dimensionality Reduction
10 Citations (Scopus)
155 Downloads (Pure)

Inference in hybrid Bayesian networks with Mixtures of Truncated Basis Functions

Langseth, H., Nielsen, T. D., Rumí, R. & Salmerón, A., 2012, Proceedings of the Sixth European Workshop on Probabilistic Graphical Models. Cano, A., Gómez-Olmedo, M. & Nielsen, T. D. (eds.). DECSAI, University of Granada, p. 171-178 8 p.

Research output: Contribution to book/anthology/report/conference proceedingBook chapterResearchpeer-review

File
Bayesian networks
Experiments

Learning Markov Decision Processes for Model Checking

Mao, H., Chen, Y., Jaeger, M., Nielsen, T. D., Larsen, K. G. & Nielsen, B., 2012, In : Electronic Proceedings in Theoretical Computer Science. 103, p. 49-63

Research output: Contribution to journalConference article in JournalResearchpeer-review

Model checking
Temporal logic
Finite automata
Learning algorithms
Learning systems
10 Citations (Scopus)
250 Downloads (Pure)

Learning Markov models for stationary system behaviors

Chen, Y., Mao, H., Jaeger, M., Nielsen, T. D., Larsen, K. G. & Nielsen, B., 2012, NASA Formal Methods: 4th International Symposium, NFM 2012, Norfolk, VA, USA, April 3-5, 2012. Proceedings. Goodloe, A. E. & Person, S. (eds.). Springer, p. 216-230 15 p. (Lecture Notes in Computer Science, Vol. 7226).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
Embedded systems
Markov processes
Learning systems
Hardware
Experiments
8 Citations (Scopus)
116 Downloads (Pure)

Learning Mixtures of Truncated Basis Functions from Data

Langseth, H., Nielsen, T. D. & Salmerón, A., 2012, Proceedings of the Sixth European Workshop on Probabilistic Graphical Models. Cano, A., Gómez-Olmedo, M. & Nielsen, T. D. (eds.). DECSAI, University of Granada, p. 163-170 8 p.

Research output: Contribution to book/anthology/report/conference proceedingBook chapterResearchpeer-review

File
Bayesian networks
Convex optimization
Maximum likelihood
48 Citations (Scopus)
447 Downloads (Pure)

Mixtures of truncated basis functions

Langseth, H., Nielsen, T. D., Rumí, R. & Salmerón, A., 2012, In : International Journal of Approximate Reasoning. 53, 2, p. 212-227

Research output: Contribution to journalJournal articleResearchpeer-review

File
Basis Functions
Approximation
Polynomials
Polynomial
Fourier series
2011
25 Citations (Scopus)
638 Downloads (Pure)

Learning Probabilistic Automata for Model Checking

Mao, H., Chen, Y., Jaeger, M., Nielsen, T. D., Larsen, K. G. & Nielsen, B., 2011, 8th International Conference on Quantitative Evaluation of Systems (QEST). IEEE, p. 111-120 10 p.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

File
10 Citations (Scopus)
553 Downloads (Pure)

Probabilistic decision graphs for optimization under uncertainty

Jensen, F. V. & Nielsen, T. D., 2011, In : 4OR. 9, 1, p. 1-28 28 p.

Research output: Contribution to journalJournal articleResearchpeer-review

File
Influence Diagrams
Decision problem
Uncertainty
Optimization
Graph in graph theory
2010

Dynamic Latent Classification Model: Towards a More Expressive Model for Dynamic Classification

Zhong, S., Martínez, A. M., Nielsen, T. D. & Langseth, H., 2010.

Research output: Contribution to conference without publisher/journalPaper without publisher/journalResearchpeer-review

Monitoring
Sensors
Oils

Editorial: Special Issue on PGM-2008

Jaeger, M. & Nielsen, T. D., Jun 2010, In : International Journal of Approximate Reasoning. 51, 5, p. 473

Research output: Contribution to journalEditorialResearch

25 Citations (Scopus)
271 Downloads (Pure)

Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials

Langseth, H., Nielsen, T. D., Rumí, R. & Salmerón, A., Jun 2010, In : International Journal of Approximate Reasoning. 51, 5, p. 485-498

Research output: Contribution to journalJournal articleResearchpeer-review

File
Model Selection
Parameter estimation
Parameter Estimation
Likelihood
Regression
4 Citations (Scopus)
173 Downloads (Pure)

Parameter learning in MTE networks using incomplete data

Fernández, A., Langseth, H., Nielsen, T. D. & Salmerón, A., 2010.

Research output: Contribution to conference without publisher/journalPaper without publisher/journalResearchpeer-review

Open Access
File
Bayesian networks
Maximum likelihood
3 Citations (Scopus)

Towards a more expressive model for dynamic classification

Zhong, S., Martínez, A. M., Nielsen, T. D. & Langseth, H., 2010, Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23. AAAI Press, p. 563-564

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2009
9 Citations (Scopus)
371 Downloads (Pure)

A comparison of two approaches for solving unconstrained influence diagrams

Ahlmann-Ohlsen, K. S., Jensen, F. V., Nielsen, T. D., Pedersen, O. & Vomlelová, M., 2009, In : International Journal of Approximate Reasoning. 50, 1, p. 153-173

Research output: Contribution to journalJournal articleResearchpeer-review

File
Influence Diagrams
Decision trees
Specifications
Decision problem
Partial ordering
280 Downloads (Pure)

A latent model for collaborative filtering

Langseth, H. & Nielsen, T. D., 2009, Aalborg Universitet: Department of Computer Science, Aalborg University, 24 p.

Research output: Working paperResearch

Open Access
File
Collaborative filtering
Recommender systems