Thomas D. Nielsen

80 papers A* 1A 4B 1C 6Misc 2Journal 45Unranked 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning. CoRR Abdulkadir Çelikkanat, Andrés R. Masegosa, Mads Albertsen, Thomas D. Nielsen
2024	—	`conf`	Compositional Control Synthesis for Water Management System. ECC Esther Hahyeon Kim, Kim G. Larsen, Martijn A. Goorden, Thomas D. Nielsen
2024	J	`jnl`	Controlling stormwater detention ponds under partial observability. J. Log. Algebraic Methods Program. Esther Hahyeon Kim, Martijn A. Goorden, Kim G. Larsen, Thomas D. Nielsen
2024	—	`conf`	Integrating Multi-Modal Spatial Data using Knowledge Graphs - a Case Study of Microflora Danica. SeWeBMeDA@ESWC Mads Corfixen, Thomas Heede, Tomer Sagi, Mads Albertsen, Thomas D. Nielsen, Katja Hose
2024	J	`jnl`	Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning. CoRR Abdulkadir Çelikkanat, Andrés R. Masegosa, Thomas D. Nielsen
2023	—	`conf`	Guaranteed safe controller synthesis for switched systems using analytical solutions CCTA Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Weizhu Qian, Michael R. Rasmussen, Guohan Zhao
2022	—	`conf`	A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. PGM Antonio Salmerón, Helge Langseth, Andrés R. Masegosa, Thomas D. Nielsen
2021	—	`conf`	Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. ADHS Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jirí Srba
2021	J	`jnl`	Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds. CoRR Martijn A. Goorden, Kim G. Larsen, Jesper E. Nielsen, Thomas D. Nielsen, Michael R. Rasmussen, Jirí Srba
2021	J	`jnl`	Probabilistic Models with Deep Neural Networks. Entropy Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2020	J	`jnl`	Analyzing concept drift: A case study in the financial sector. Intell. Data Anal. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2020	J	`jnl`	MoTBFs: An R Package for Learning Hybrid Bayesian Networks Using Mixtures of Truncated Basis Functions. R J. Inmaculada Pérez-Bernabé, Ana D. Maldonado, Antonio Salmerón, Thomas D. Nielsen
2020	—	`conf`	Preface. PGM Thomas D. Nielsen, Manfred Jaeger
2019	J	`jnl`	AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2019	J	`jnl`	Probabilistic Models with Deep Neural Networks. CoRR Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
2018	J	`jnl`	A Review of Inference Algorithms for Hybrid Bayesian Networks. J. Artif. Intell. Res. Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2018	J	`jnl`	Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. Int. J. Approx. Reason. Darío Ramos-López, Andrés R. Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen
2017	J	`jnl`	A parallel algorithm for Bayesian network structure learning from large data sets. Knowl. Based Syst. Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen
2017	J	`jnl`	AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	A*	`conf`	Bayesian Models of Data Streams with Hierarchical Power Priors. ICML Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
2017	J	`jnl`	Bayesian Models of Data Streams with Hierarchical Power Priors. CoRR Andrés R. Masegosa, Thomas D. Nielsen, Helge Langseth, Darío Ramos-López, Antonio Salmerón, Anders L. Madsen
2017	J	`jnl`	MAP inference in dynamic hybrid Bayesian networks. Prog. Artif. Intell. Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2017	J	`jnl`	Scaling up Bayesian variational inference using distributed computing clusters. Int. J. Approx. Reason. Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2016	J	`jnl`	A scalable pairwise class interaction framework for multidimensional classification. Int. J. Approx. Reason. Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José M. Puerta
2016	J	`jnl`	Anytime Decision Making Based on Unconstrained Influence Diagrams. Int. J. Intell. Syst. Manuel Luque, Thomas D. Nielsen, Finn Verner Jensen
2016	—	`conf`	Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
2016	J	`jnl`	Learning deterministic probabilistic automata from a model checking perspective. Mach. Learn. Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
2016	A	`conf`	Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. ECAI Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen, Darío Ramos-López, Ana M. Martínez, Andrés R. Masegosa
2016	—	`conf`	Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. Probabilistic Graphical Models Darío Ramos-López, Antonio Salmerón, Rafael Rumí, Ana M. Martínez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen
2016	—	`conf`	d-VMP: Distributed Variational Message Passing. Probabilistic Graphical Models Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2015	J	`jnl`	Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza
2015	—	`conf`	Dynamic Bayesian modeling for risk prediction in credit operations. SCAI Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
2015	C	`conf`	MPE Inference in Conditional Linear Gaussian Networks. ECSQARU Antonio Salmerón, Rafael Rumí, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
2015	B	`conf`	Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. IDA Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Antonio Fernández, Anders L. Madsen, Ramón Sáez
2015	—	`conf`	Parallel Importance Sampling in Conditional Linear Gaussian Networks. CAEPIA Antonio Salmerón, Darío Ramos-López, Hanen Borchani, Ana M. Martínez, Andrés R. Masegosa, Antonio Fernández, Helge Langseth, Anders L. Madsen, Thomas D. Nielsen
2015	—	`conf`	Parallelisation of the PC Algorithm. CAEPIA Anders L. Madsen, Frank Jensen, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen
2015	J	`jnl`	Scalable learning of probabilistic latent models for collaborative filtering. Decis. Support Syst. Helge Langseth, Thomas D. Nielsen
2014	—	`conf`	A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. Probabilistic Graphical Models Anders L. Madsen, Frank Jensen, Antonio Salmerón, Martin Karlsen, Helge Langseth, Thomas D. Nielsen
2014	—	`conf`	A Pairwise Class Interaction Framework for Multilabel Classification. Probabilistic Graphical Models Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José M. Puerta
2014	J	`jnl`	Learning mixtures of truncated basis functions from data. Int. J. Approx. Reason. Helge Langseth, Thomas D. Nielsen, Inmaculada Pérez-Bernabé, Antonio Salmerón
2014	—	`conf`	Requirement Engineering for a Small Project with Pre-Specified Scope. NIK Thomas D. Nielsen, Sigve Hovda, Antonio Fernández, Helge Langseth, Anders L. Madsen, Andrés R. Masegosa, Antonio Salmerón
2013	—	`conf`	Learning Mixtures of Polynomials of Conditional Densities from Data. CAEPIA Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga
2013	J	`jnl`	Representing and Solving Asymmetric Bayesian Decision Problems CoRR Thomas D. Nielsen, Finn Verner Jensen
2013	J	`jnl`	Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example CoRR Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff
2013	J	`jnl`	Welldefined Decision Scenarios CoRR Thomas D. Nielsen, Finn Verner Jensen
2012	—	`conf`	Learning Markov Decision Processes for Model Checking QFM Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
2012	J	`jnl`	Mixtures of truncated basis functions. Int. J. Approx. Reason. Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2011	—	`conf`	Learning Probabilistic Automata for Model Checking. QEST Hua Mao, Yingke Chen, Manfred Jaeger, Thomas D. Nielsen, Kim G. Larsen, Brian Nielsen
2011	J	`jnl`	Probabilistic decision graphs for optimization under uncertainty. 4OR Finn Verner Jensen, Thomas D. Nielsen
2010	J	`jnl`	Parameter estimation and model selection for mixtures of truncated exponentials. Int. J. Approx. Reason. Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2010	J	`jnl`	Special Issue on PGM-2008. Int. J. Approx. Reason. Manfred Jaeger, Thomas D. Nielsen
2010	Misc	`conf`	Towards a More Expressive Model for Dynamic Classification. FLAIRS Shengtong Zhong, Ana M. Martínez, Thomas D. Nielsen, Helge Langseth
2009	J	`jnl`	A comparison of two approaches for solving unconstrained influence diagrams. Int. J. Approx. Reason. Kristian S. Ahlmann-Ohlsen, Finn Verner Jensen, Thomas D. Nielsen, Ole Pedersen, Marta Vomlelová
2009	J	`jnl`	Inference in hybrid Bayesian networks. Reliab. Eng. Syst. Saf. Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2009	J	`jnl`	Latent classification models for binary data. Pattern Recognit. Helge Langseth, Thomas D. Nielsen
2009	C	`conf`	Maximum Likelihood Learning of Conditional MTE Distributions. ECSQARU Helge Langseth, Thomas D. Nielsen, Rafael Rumí, Antonio Salmerón
2008	J	`jnl`	Adapting Bayes network structures to non-stationary domains. Int. J. Approx. Reason. Søren Holbech Nielsen, Thomas D. Nielsen
2007	J	`jnl`	On-line alert systems for production plants: A conflict based approach. Int. J. Approx. Reason. Thomas D. Nielsen, Finn Verner Jensen
2006	C	`conf`	A COTS framework for sensor fusion using dynamic bayesian networks in livestock production. CAINE Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler
2006	—	`conf`	Adapting Bayes Network Structures to Non-stationary Domains. Probabilistic Graphical Models Søren Holbech Nielsen, Thomas D. Nielsen
2006	J	`jnl`	Classification using Hierarchical Naïve Bayes models. Mach. Learn. Helge Langseth, Thomas D. Nielsen
2006	J	`jnl`	Dynamic decision making without expected utility: An operational approach. Eur. J. Oper. Res. Thomas D. Nielsen, Jean-Yves Jaffray
2006	—	`conf`	Sensor Fusion Using Dynamic Bayesian Networks in Livestock Production Buildings. CIMCA/IAWTIC Jens A. Hansen, Thomas D. Nielsen, Henrik Schiøler
2006	J	`jnl`	Sequential influence diagrams: A unified asymmetry framework. Int. J. Approx. Reason. Finn Verner Jensen, Thomas D. Nielsen, Prakash P. Shenoy
2005	C	`conf`	Alert Systems for Production Plants: A Methodology Based on Conflict Analysis. ECSQARU Thomas D. Nielsen, Finn Verner Jensen
2005	J	`jnl`	Latent Classification Models. Mach. Learn. Helge Langseth, Thomas D. Nielsen
2005	J	`jnl`	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. Thomas D. Nielsen, Nevin Lianwen Zhang
2004	J	`jnl`	Latent variable discovery in classification models. Artif. Intell. Medicine Nevin Lianwen Zhang, Thomas D. Nielsen, Finn Verner Jensen
2004	J	`jnl`	Learning a decision maker's utility function from (possibly) inconsistent behavior. Artif. Intell. Thomas D. Nielsen, Finn Verner Jensen
2003	J	`jnl`	Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. J. Mach. Learn. Res. Helge Langseth, Thomas D. Nielsen
2003	J	`jnl`	Representing and Solving Asymmetric Decision Problems. Int. J. Inf. Technol. Decis. Mak. Thomas D. Nielsen, Finn Verner Jensen
2003	J	`jnl`	Sensitivity analysis in influence diagrams. IEEE Trans. Syst. Man Cybern. Part A Thomas D. Nielsen, Finn Verner Jensen
2003	C	`ed.`	Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 7th European Conference, ECSQARU 2003, Aalborg, Denmark, July 2-5, 2003. Proceedings ECSQARU Thomas D. Nielsen, Nevin Lianwen Zhang
2002	J	`jnl`	Decomposition of influence diagrams. J. Appl. Non Class. Logics Thomas D. Nielsen
2001	—	`conf`	Cutting Influence Diagrams Down to the Core. SCAI Thomas D. Nielsen, Finn Verner Jensen
2001	C	`conf`	Decomposition of Influence Diagrams. ECSQARU Thomas D. Nielsen
2001	Misc	`conf`	Structural Learning in Object Oriented Domains. FLAIRS Olav Bangsø, Helge Langseth, Thomas D. Nielsen
2000	A	`conf`	Representing and Solving Asymmetric Bayesian Decision Problems. UAI Thomas D. Nielsen, Finn Verner Jensen
2000	A	`conf`	Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example. UAI Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe Kjærulff
1999	A	`conf`	Welldefined Decision Scenarios. UAI Thomas D. Nielsen, Finn Verner Jensen