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2020 – today
- 2023
[c40]Emil Riis Hansen
, Thomas Dyhre Nielsen, Thomas Mulvad, Mads Nibe Stausholm, Tomer Sagi, Katja Hose:
Patient Event Sequences for Predicting Hospitalization Length of Stay. AIME 2023: 51-56- [c39]Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Weizhu Qian, Michael R. Rasmussen, Guohan Zhao:
Guaranteed safe controller synthesis for switched systems using analytical solutions*. CCTA 2023: 784-790 - [c38]Andre Lamurias, Alessandro Tibo, Katja Hose, Mads Albertsen, Thomas Dyhre Nielsen:
Metagenomic Binning using Connectivity-constrained Variational Autoencoders. ICML 2023: 18471-18481 - [i16]Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad, Mads Nibe Stausholm, Tomer Sagi, Katja Hose:
Hospitalization Length of Stay Prediction using Patient Event Sequences. CoRR abs/2303.11042 (2023) - [i15]Anders Mariegaard, Kim G. Larsen, Marco Muñiz, Thomas Dyhre Nielsen:
Energy Consumption Optimization in Radio Access Networks (ECO-RAN). CoRR abs/2304.00277 (2023) - 2022
- [j44]Andre Lamurias, Mantas Sereika, Mads Albertsen, Katja Hose, Thomas Dyhre Nielsen:
Metagenomic binning with assembly graph embeddings. Bioinform. 38(19): 4481-4487 (2022) - [j43]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
Relational Fusion Networks: Graph Convolutional Networks for Road Networks. IEEE Trans. Intell. Transp. Syst. 23(1): 418-429 (2022) - [j42]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
UniTE - The Best of Both Worlds: Unifying Function-fitting and Aggregation-based Approaches to Travel Time and Travel Speed Estimation. ACM Trans. Spatial Algorithms Syst. 8(4): 30:1-30:26 (2022) - [c37]Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen:
A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM 2022: 205-216 - [i14]Alessandro Tibo, Thomas Dyhre Nielsen:
Inducing Gaussian Process Networks. CoRR abs/2204.09889 (2022) - [i13]Andre Lamurias, Alessandro Tibo, Katja Hose, Mads Albertsen, Thomas Dyhre Nielsen:
Graph Neural Networks for Microbial Genome Recovery. CoRR abs/2204.12270 (2022) - 2021
- [j41]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. Entropy 23(1): 117 (2021) - [j40]Anders Bruun, Effie Lai-Chong Law, Thomas Dyhre Nielsen, Matthias Heintz:
Do You Feel the Same? On the Robustness of Cued-Recall Debriefing for User Experience Evaluation. ACM Trans. Comput. Hum. Interact. 28(4): 25:1-25:45 (2021) - [c36]Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jirí Srba:
Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. ADHS 2021: 13-18 - [c35]Laurynas Siksnys, Torben Bach Pedersen, Thomas Dyhre Nielsen, Davide Frazzetto:
SolveDB+: SQL-Based Prescriptive Analytics. EDBT 2021: 133-144 - [i12]Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jirí Srba:
Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. CoRR abs/2104.12509 (2021) - [i11]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
UniTE - The Best of Both Worlds: Unifying Function-Fitting and Aggregation-Based Approaches to Travel Time and Travel Speed Estimation. CoRR abs/2104.13321 (2021) - 2020
- [j39]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. 24(3): 665-688 (2020) - [j38]Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón, Thomas D. Nielsen:
MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions. R J. 12(2): 321 (2020) - [c34]Thomas D. Nielsen, Manfred Jaeger:
Preface. PGM 2020: 1-4 - [e3]Manfred Jaeger, Thomas Dyhre Nielsen:
International Conference on Probabilistic Graphical Models, PGM 2020, 23-25 September 2020, Aalborg, Hotel Comwell Rebild Bakker, Skørping, Denmark. Proceedings of Machine Learning Research 138, PMLR 2020 [contents] - [i10]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
Relational Fusion Networks: Graph Convolutional Networks for Road Networks. CoRR abs/2006.09030 (2020)
2010 – 2019
- 2019
- [j37]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163: 595-597 (2019) - [j36]Davide Frazzetto, Thomas Dyhre Nielsen, Torben Bach Pedersen, Laurynas Siksnys:
Prescriptive analytics: a survey of emerging trends and technologies. VLDB J. 28(4): 575-595 (2019) - [c33]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
Graph Convolutional Networks for Road Networks. SIGSPATIAL/GIS 2019: 460-463 - [i9]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. CoRR abs/1908.03442 (2019) - [i8]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
Graph Convolutional Networks for Road Networks. CoRR abs/1908.11567 (2019) - [i7]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen:
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network. CoRR abs/1911.06217 (2019) - 2018
- [j35]Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. 100: 115-134 (2018) - [j34]Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
A Review of Inference Algorithms for Hybrid Bayesian Networks. J. Artif. Intell. Res. 62: 799-828 (2018) - [c32]Tobias Skovgaard Jepsen, Christian S. Jensen, Thomas Dyhre Nielsen, Kristian Torp:
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network. IEEE BigData 2018: 3422-3431 - [c31]Davide Frazzetto, Bijay Neupane, Torben Bach Pedersen, Thomas Dyhre Nielsen:
Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices. e-Energy 2018: 1-11 - [i6]Davide Frazzetto, Bijay Neupane, Torben Bach Pedersen, Thomas Dyhre Nielsen:
Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices. CoRR abs/1805.05470 (2018) - 2017
- [j33]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. 88: 435-451 (2017) - [j32]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. 117: 46-55 (2017) - [j31]Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. 6(2): 133-144 (2017) - [c30]Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. ICML 2017: 2334-2343 - [i5]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR abs/1704.01427 (2017) - [i4]Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen:
Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR abs/1707.02293 (2017) - 2016
- [j30]Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta:
A scalable pairwise class interaction framework for multidimensional classification. Int. J. Approx. Reason. 68: 194-210 (2016) - [j29]Manuel Luque, Thomas D. Nielsen, Finn Verner Jensen:
Anytime Decision Making Based on Unconstrained Influence Diagrams. Int. J. Intell. Syst. 31(4): 379-398 (2016) - [j28]Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning deterministic probabilistic automata from a model checking perspective. Mach. Learn. 105(2): 255-299 (2016) - [c29]Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa:
Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI 2016: 743-750 - [c28]Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops 2016: 1284-1287 - [c27]Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen:
d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models 2016: 321-332 - [c26]Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen:
Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models 2016: 415-425 - 2015
- [j27]Helge Langseth, Thomas D. Nielsen:
Scalable learning of probabilistic latent models for collaborative filtering. Decis. Support Syst. 74: 1-11 (2015) - [j26]Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza:
Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. 30(3): 236-264 (2015) - [c25]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen:
Parallelisation of the PC Algorithm. CAEPIA 2015: 14-24 - [c24]Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA 2015: 36-46 - [c23]Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen:
MPE Inference in Conditional Linear Gaussian Networks. ECSQARU 2015: 407-416 - [c22]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA 2015: 72-83 - [c21]Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez:
Dynamic Bayesian modeling for risk prediction in credit operations. SCAI 2015: 17-26 - 2014
- [j25]Andrés Cano, Manuel Gómez-Olmedo, Thomas Dyhre Nielsen:
Special Issue on PGM-2012. Int. J. Approx. Reason. 55(4): 925 (2014) - [j24]Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón:
Learning mixtures of truncated basis functions from data. Int. J. Approx. Reason. 55(4): 940-956 (2014) - [j23]Shengtong Zhong, Helge Langseth, Thomas Dyhre Nielsen:
A classification-based approach to monitoring the safety of dynamic systems. Reliab. Eng. Syst. Saf. 121: 61-71 (2014) - [c20]Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón:
Requirement Engineering for a Small Project with Pre-Specified Scope. NIK 2014 - [c19]Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta:
A Pairwise Class Interaction Framework for Multilabel Classification. Probabilistic Graphical Models 2014: 17-32 - [c18]Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen:
A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models 2014: 302-317 - 2013
- [j22]Finn Verner Jensen, Thomas Dyhre Nielsen:
Probabilistic decision graphs for optimization under uncertainty. Ann. Oper. Res. 204(1): 223-248 (2013) - [c17]Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga:
Learning Mixtures of Polynomials of Conditional Densities from Data. CAEPIA 2013: 363-372 - [e2]Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani:
Twelfth Scandinavian Conference on Artificial Intelligence, SCAI 2013, Aalborg, Denmark, November 20-22, 2013. Frontiers in Artificial Intelligence and Applications 257, IOS Press 2013, ISBN 978-1-61499-329-2 [contents] - [i3]Thomas D. Nielsen, Finn Verner Jensen:
Representing and Solving Asymmetric Bayesian Decision Problems. CoRR abs/1301.3879 (2013) - [i2]Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff:
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. CoRR abs/1301.3880 (2013) - [i1]Thomas D. Nielsen, Finn Verner Jensen:
Welldefined Decision Scenarios. CoRR abs/1301.6729 (2013) - 2012
- [j21]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Mixtures of truncated basis functions. Int. J. Approx. Reason. 53(2): 212-227 (2012) - [j20]Helge Langseth, Thomas Dyhre Nielsen:
A latent model for collaborative filtering. Int. J. Approx. Reason. 53(4): 447-466 (2012) - [c16]Yingke Chen, Thomas Dyhre Nielsen:
Active Learning of Markov Decision Processes for System Verification. ICMLA (2) 2012: 289-294 - [c15]Yingke Chen, Hua Mao, Manfred Jaeger, Thomas Dyhre Nielsen, Kim Guldstrand Larsen, Brian Nielsen:
Learning Markov Models for Stationary System Behaviors. NASA Formal Methods 2012: 216-230 - [c14]Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning Markov Decision Processes for Model Checking. QFM 2012: 49-63 - 2011
- [j19]Finn Verner Jensen, Thomas D. Nielsen:
Probabilistic decision graphs for optimization under uncertainty. 4OR 9(1): 1-28 (2011) - [c13]Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen:
Learning Probabilistic Automata for Model Checking. QEST 2011: 111-120 - 2010
- [j18]Manfred Jaeger, Thomas D. Nielsen:
Special Issue on PGM-2008. Int. J. Approx. Reason. 51(5): 473 (2010) - [j17]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reason. 51(5): 485-498 (2010) - [c12]Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth:
Towards a More Expressive Model for Dynamic Classification. FLAIRS 2010
2000 – 2009
- 2009
- [j16]Kristian S. Ahlmann-Ohlsen, Finn Verner Jensen, Thomas D. Nielsen, Ole Pedersen, Marta Vomlelová:
A comparison of two approaches for solving unconstrained influence diagrams. Int. J. Approx. Reason. 50(1): 153-173 (2009) - [j15]Helge Langseth, Thomas D. Nielsen:
Latent classification models for binary data. Pattern Recognit. 42(11): 2724-2736 (2009) - [j14]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Inference in hybrid Bayesian networks. Reliab. Eng. Syst. Saf. 94(10): 1499-1509 (2009) - [c11]Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón:
Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU 2009: 240-251 - 2008
- [j13]Søren Holbech Nielsen, Thomas D. Nielsen:
Adapting Bayes network structures to non-stationary domains. Int. J. Approx. Reason. 49(2): 379-397 (2008) - 2007
- [j12]Thomas D. Nielsen, Finn Verner Jensen:
On-line alert systems for production plants: A conflict based approach. Int. J. Approx. Reason. 45(2): 255-270 (2007) - 2006
- [j11]Thomas D. Nielsen, Jean-Yves Jaffray:
Dynamic decision making without expected utility: An operational approach. Eur. J. Oper. Res. 169(1): 226-246 (2006) - [j10]Finn Verner Jensen, Thomas D. Nielsen, Prakash P. Shenoy:
Sequential influence diagrams: A unified asymmetry framework. Int. J. Approx. Reason. 42(1-2): 101-118 (2006) - [j9]Helge Langseth, Thomas D. Nielsen:
Classification using Hierarchical Naïve Bayes models. Mach. Learn. 63(2): 135-159 (2006) - [c10]Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler:
A COTS framework for sensor fusion using dynamic bayesian networks in livestock production. CAINE 2006: 41-47 - [c9]Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler:
Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings. CIMCA/IAWTIC 2006: 215 - [c8]Søren Holbech Nielsen, Thomas D. Nielsen:
Adapting Bayes Network Structures to Non-stationary Domains. Probabilistic Graphical Models 2006: 223-230 - 2005
- [j8]Thomas D. Nielsen, Nevin Lianwen Zhang:
Special Issue on ECSQARU-2003: The Seventh European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty: Message from the Guest Editors. Int. J. Approx. Reason. 38(3): 215-216 (2005) - [j7]Helge Langseth, Thomas D. Nielsen:
Latent Classification Models. Mach. Learn. 59(3): 237-265 (2005) - [c7]Thomas D. Nielsen, Finn Verner Jensen:
Alert Systems for Production Plants: A Methodology Based on Conflict Analysis. ECSQARU 2005: 76-87 - 2004
- [j6]Thomas D. Nielsen, Finn Verner Jensen:
Learning a decision maker's utility function from (possibly) inconsistent behavior. Artif. Intell. 160(1-2): 53-78 (2004) - [j5]Nevin Lianwen Zhang, Thomas D. Nielsen, Finn Verner Jensen:
Latent variable discovery in classification models. Artif. Intell. Medicine 30(3): 283-299 (2004) - 2003
- [j4]Thomas D. Nielsen, Finn Verner Jensen:
Representing and Solving Asymmetric Decision Problems. Int. J. Inf. Technol. Decis. Mak. 2(2): 217-263 (2003) - [j3]Helge Langseth, Thomas D. Nielsen:
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. J. Mach. Learn. Res. 4: 339-368 (2003) - [j2]Thomas D. Nielsen, Finn Verner Jensen:
Sensitivity analysis in influence diagrams. IEEE Trans. Syst. Man Cybern. Part A 33(2): 223-234 (2003) - [e1]Thomas D. Nielsen, Nevin Lianwen Zhang:
Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 7th European Conference, ECSQARU 2003, Aalborg, Denmark, July 2-5, 2003. Proceedings. Lecture Notes in Computer Science 2711, Springer 2003, ISBN 3-540-40494-5 [contents] - 2002
- [j1]Thomas D. Nielsen:
Decomposition of influence diagrams. J. Appl. Non Class. Logics 12(2): 135-150 (2002) - 2001
- [c6]Thomas D. Nielsen:
Decomposition of Influence Diagrams. ECSQARU 2001: 144-155 - [c5]Olav Bangsø, Helge Langseth, Thomas D. Nielsen:
Structural Learning in Object Oriented Domains. FLAIRS 2001: 340-344 - [c4]Thomas D. Nielsen, Finn Verner Jensen:
Cutting Influence Diagrams Down to the Core. SCAI 2001: 159-160 - 2000
- [c3]Thomas D. Nielsen, Finn Verner Jensen:
Representing and Solving Asymmetric Bayesian Decision Problems. UAI 2000: 416-425 - [c2]Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff:
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. UAI 2000: 426-435
1990 – 1999
- 1999
- [c1]Thomas D. Nielsen, Finn Verner Jensen:
Welldefined Decision Scenarios. UAI 1999: 502-511
Coauthor Index
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