  | Bernard, F., Vlassis, N., Gemmar, P., Husch, A., Thunberg, J., Goncalves, J., & Hertel, F. (2016). Fast Correspondences for Statistical Shape Models of Brain Structures. In SPIE Medical Imaging. doi:10.1117/12.2206024  Peer reviewed |
  | Salamanca Mino, L., Vlassis, N., Diederich, N., Bernard, F., & Skupin, A. (2015). Improved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors. In Improved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors (pp. 119-126). Peer reviewed |
 | Colombo, N., & Vlassis, N. (2015). FastMotif: Spectral Sequence Motif Discovery. Bioinformatics. doi:10.1093/bioinformatics/btv208 Peer reviewed |
 | Laczny, C. C., Sternal, T., Plugaru, V., Gawron, P., Atashpendar, A., Margossian, H. H., Coronado, S., van der Maaten, L., Vlassis, N., & Wilmes, P. (2015). VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data. Microbiome. doi:10.1186/s40168-014-0066-1 Peer Reviewed verified by ORBi |
 | Vlassis, N., & Glaab, E. (2015). GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net. Statistical Applications in Genetics and Molecular Biology, 14 (2), 221-224. doi:10.1515/sagmb-2014-0045 Peer Reviewed verified by ORBi |
 | Muller, E., Pinel, N., Laczny, C. C., Hoopmann, M., Narayanasamy, S., Lebrun, L., Roume, H., Lin, J., May, P., Hicks, N., Buschart, A., Wampach, L., Liu, C., Price, L., Gillece, J., Guignard, C., Schupp, J., Vlassis, N., Baliga, ... Wilmes, P. (2014). Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage. Nature Communications. doi:10.1038/ncomms6603 Peer Reviewed verified by ORBi |
 | Vlassis, N., & Jungers, R. (May 2014). Polytopic uncertainty for linear systems: New and old complexity results. Systems and Control Letters, 67, 9-13. doi:10.1016/j.sysconle.2014.02.001 Peer Reviewed verified by ORBi |
 | Laczny, C. C., Pinel, N., Vlassis, N., & Wilmes, P. (2014). Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction. Scientific Reports. doi:10.1038/srep04516 Peer Reviewed verified by ORBi |
 | Laczny, C. C., May, P., Vlassis, N., & Wilmes, P. (March 2014). Identification of condition-specific microbial populations from human metagenomic data [Poster presentation]. VizBi - Visualizing biological data 2014, Heidelberg, Germany. |
 | Colombo, N., & Vlassis, N. (2014). Spectral Sequence Motif Discovery. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/21010. |
 | Thiele, I., Vlassis, N., & Fleming, R. M. (2014). FASTGAPFILL: Efficient gap filling in metabolic networks. Bioinformatics, 30 (17), 2529-2531. doi:10.1093/bioinformatics/btu321 Peer reviewed |
 | Vlassis, N., Pacheco, M., & Sauter, T. (January 2014). Fast reconstruction of compact context-specific metabolic network models. PLoS Computational Biology, 10 (1), 1003424. doi:10.1371/journal.pcbi.1003424 Peer Reviewed verified by ORBi |
Muller, E., Pinel, N., Laczny, C. C., Hoopmann, M., Lebrun, L., Roume, H., May, P., Hicks, N., Liu, C., Price, L., Gillece, J., Guignard, C., Schupp, J., Vlassis, N., Moritz, R., Baliga, N., Keim, P., & Wilmes, P. (2014). Community integrated omics links the dominance of a microbial generalist to fine-tuned resource usage [Paper presentation]. 15th International Symposium on Microbial Ecology. |
Muller, E., Pinel, N., Laczny, C. C., Hoopmann, M., Lebrun, L., Roume, H., May, P., Hicks, N., Liu, C., Price, L., Gillece, J., Guignard, C., Schupp, J., Vlassis, N., Moritz, R., Baliga, N., Keim, P., & Wilmes, P. (2014). Community integrated omics links the dominance of a microbial generalist to fine-tuned resource usage [Poster presentation]. Phenotypic heterogeneity and sociobiology of bacterial populations. |
Blazewicz, J., Kasprzak, M., & Vlassis, N. (2013). Ties between graph theory and biology. In J. L. Gross, J. Yellen, ... P. Zhang (Eds.), Handbook of Graph Theory (2nd). Chapman and Hall/CRC. Peer reviewed |
 | Ballereau, S., Glaab, E., Kolodkin, A., Chaiboonchoe, A., Biryukov, M., Vlassis, N., Ahmed, H., Pellet, J., Baliga, N., Hood, L., Schneider, R., Balling, R., & Auffray, C. (2013). Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology. In A. Prokop & B. Csukás (Eds.), Systems Biology: Integrative Biology and Simulation Tools. Springer. doi:10.1007/978-94-007-6803-1_1 Peer reviewed |
 | Muller, E., Glaab, E., May, P., Vlassis, N., & Wilmes, P. (2013). Condensing the omics fog of microbial communities. Trends in Microbiology, 21 (7), 325–333. doi:10.1016/j.tim.2013.04.009 Peer reviewed |
 | Vlassis, N., & Glaab, E. (2013). Network deregulation analysis in complex diseases via the pairwise elastic net. In Proc 8th BeNeLux Bioinformatics Conference. doi:10.1515/sagmb-2014-0045 Peer reviewed |
Vlassis, N., Pacheco, M., & Sauter, T. (2013). Fast reconstruction of compact context-specific metabolic network models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/11095. |
 | Vlassis, N., Pacheco, M., & Sauter, T. (2013). Fastcore: An algorithm for fast reconstruction of context-specific metabolic network models. In Proc. 8th BeNeLux Bioinformatics Conference. Peer reviewed |
Vlassis, N., & Jungers, R. M. (2013). Polytopic uncertainty for linear systems: New and old complexity results. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/11098. |
Vlassis, N., Littman, M. L., & Barber, D. (2012). Stochastic POMDP controllers: How easy to optimize? [Paper presentation]. 10th European Workshop on Reinforcement Learning, Edinburgh, United Kingdom. |
 | Vlassis, N., Littman, M. L., & Barber, D. (2012). On the computational complexity of stochastic controller optimization in POMDPs. ACM Transactions on Computation Theory, 4 (4), 1-9. doi:10.1145/2382559.2382563 Peer Reviewed verified by ORBi |
 | Antony, P., Balling, R., & Vlassis, N. (2012). From Systems Biology to Systems Biomedicine. Current Opinion in Biotechnology, 23 (4), 604-8. doi:10.1016/j.copbio.2011.11.009 Peer Reviewed verified by ORBi |
 | Vlassis, N., Ghavamzadeh, M., Mannor, S., & Poupart, P. (2012). Bayesian Reinforcement Learning. In M. Wiering & M. van Otterlo (Eds.), Reinforcement Learning: State of the Art (pp. 359-386). Springer. doi:10.1007/978-3-642-27645-3_11 Peer reviewed |
Vlassis, N., Littman, M. L., & Barber, D. (2011). On the computational complexity of stochastic controller optimization in POMDPs. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/3388. |
 | Sifniotis, M., Jackson, B. J. C., Mania, K., Vlassis, N., Watten, P. L., & White, M. (2010). 3D visualization of archaeological uncertainty. In Proc. ACM Symposium on Applied Perception (pp. 162). Peer reviewed |
 | Vlassis, N., Toussaint, M., Kontes, G., & Piperidis, S. (2009). Learning model-free robot control by a Monte Carlo EM algorithm. Autonomous Robots, 27 (2), 123-130. doi:10.1007/s10514-009-9132-0 Peer reviewed |
 | Vlassis, N., & Toussaint, M. (2009). Model-free reinforcement learning as mixture learning. In Proceedings of the 26th International Conference on Machine Learning (pp. 1081-1088). Peer reviewed |
 | Oliehoek, F. A., Kooij, J. F. P., & Vlassis, N. (2008). The cross-entropy method for policy search in decentralized POMDPs. Informatica, 32 (4), 341-357. Peer reviewed |
 | Oliehoek, F. A., Spaan, M. T. J., & Vlassis, N. (2008). Optimal and approximate Q-value functions for decentralized POMDPs. Journal of Artificial Intelligence Research, 32, 289-353. doi:10.1613/jair.2447 Peer Reviewed verified by ORBi |
 | Kuyer, L., Whiteson, S., Bakker, B., & Vlassis, N. (2008). Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs. In Proceedings of 19th European Conference on Machine Learning (pp. 656-671). Peer reviewed |
 | Oliehoek, F. A., Spaan, M. T. J., Vlassis, N., & Whiteson, S. (2008). Exploiting locality of interaction in factored Dec-POMDPs. In Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (pp. 517-524). Peer reviewed |
 | Spaan, M. T. J., Oliehoek, F. A., & Vlassis, N. (2008). Multiagent Planning under Uncertainty with Stochastic Communication Delays. In 338 Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008) (pp. 338-345). Peer reviewed |
 | Poupart, P., & Vlassis, N. (2008). Model-based Bayesian reinforcement learning in partially observable domains. In Proc Int. Symp. on Artificial Intelligence and Mathematics (pp. 1-2). Peer reviewed |
Vlassis, N. (2007). Distributed Decision Making for Robot Teams. In Proc. 1st Int. Symp. on Intelligent and Distributed Computing. Peer reviewed |
 | Oliehoek, F. A., & Vlassis, N. (2007). Q-value functions for decentralized POMDPs. In Proc Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (pp. 833-840). Peer reviewed |
 | Kurihara, K., Welling, M., & Vlassis, N. (2007). Accelerated variational dirichlet process mixtures. In Advances in Neural Information Processing Systems 19 (pp. 761-768). MIT Press. Peer reviewed |
Diplaros, A., Vlassis, N., & Gevers, T. (2007). A spatially constrained generative model and an EM algorithm for image segmentation. IEEE Transactions on Neural Networks, 18 (3), 798-808. doi:10.1109/TNN.2007.891190 Peer Reviewed verified by ORBi |
Vlassis, N. (2007). A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. San Rafael, United States - California: Morgan & Claypool. |
Oliehoek, F. A., & Vlassis, N. (2007). Q-value Heuristics for Approximate Solutions of Dec-POMDPs. In Proc. AAAI Spring Symp. on Game Theoretic and Decision Theoretic Agents. Peer reviewed |
 | Porta, J. M., Vlassis, N., Spaan, M. T. J., & Poupart, P. (2006). Point-Based Value Iteration for Continuous POMDPs. Journal of Machine Learning Research, 7, 2329-2367. Peer Reviewed verified by ORBi |
 | Spaan, M. T. J., Gordon, G. J., & Vlassis, N. (2006). Decentralized planning under uncertainty for teams of communicating agents. In Proc. Int. Joint Conf. on Autonomous Agents and Multiagent Systems, Hakodate, Japan (pp. 249-256). Peer reviewed |
 | Kok, J. R., & Vlassis, N. (2006). Using the max-plus algorithm for multiagent decision making in coordination graphs. In Proc. RoboCup Int. Symposium, Osaka, Japan (pp. 1-12). Peer reviewed |
Verbeek, J. J., & Vlassis, N. (2006). Gaussian fields for semi-supervised regression and correspondence learning. Pattern Recognition, 39 (10), 1864-1875. doi:10.1016/j.patcog.2006.04.011 Peer Reviewed verified by ORBi |
 | Oliehoek, F. A., de Jong, E. D., & Vlassis, N. (2006). The Parallel Nash Memory for Asymmetric Games. In Proc. Genetic and Evolutionary Computation Conference, Seattle, WA (pp. 337-344). Peer reviewed |
 | Poupart, P., Vlassis, N., Hoey, J., & Regan, K. (2006). An analytic solution to discrete Bayesian reinforcement learning. In Proc Int. Conf. on Machine Learning, Pittsburgh, USA (pp. 697-704). Peer reviewed |
 | James, M. R., Wessling, T., & Vlassis, N. (2006). Improving Approximate Value Iteration Using Memories and Predictive State Representations. In Proc. National Conf. on Artificial Intelligence. Peer reviewed |
 | Kok, J. R., & Vlassis, N. (2006). Collaborative multiagent reinforcement learning by payoff propagation. Journal of Machine Learning Research, 7, 1789-1828. Peer Reviewed verified by ORBi |
 | Verbeek, J. J., Nunnink, J. R. J., & Vlassis, N. (2006). Accelerated EM-based clustering of large data sets. Data Mining & Knowledge Discovery, 13 (3), 291-307. doi:10.1007/s10618-005-0033-3 Peer reviewed |
 | Verbeek, J. J., Vlassis, N., & Krose, B. J. A. (2005). Self-organizing mixture models. Neurocomputing, 63, 99-123. doi:10.1016/j.neucom.2004.04.008 Peer Reviewed verified by ORBi |
 | Porta, J. M., Spaan, M. T. J., & Vlassis, N. (2005). Robot planning in partially observable continuous domains. In Proc. Robotics: Science and Systems (pp. 217-224). Peer reviewed |
Spaan, M. T. J., & Vlassis, N. (2005). Planning with Continuous Actions in Partially Observable Environments. In Proc. IEEE Int. Conf. on Robotics and Automation (pp. 3458-3463). Peer reviewed |
 | Vlassis, N., Sfakianakis, Y., & Kowalczyk, W. (2005). Gossip-based greedy Gaussian mixture learning. In Lecture Notes in Computer Science (pp. 349-359). Springer-Verlag. doi:10.1007/11573036_33 Peer reviewed |
 | Spaan, M. T. J., & Vlassis, N. (2005). Perseus: Randomized point-based value iteration for POMDPs. Journal of Artificial Intelligence Research, 24, 195-220. doi:10.1613/jair.1659 Peer Reviewed verified by ORBi |
 | Kok, J. R., Hoen, E. J., Bakker, B., & Vlassis, N. (2005). Utile coordination: Learning interdependencies among cooperative agents. In EEE Symp. on Computational Intelligence and Games, Colchester, Essex (pp. 29-36). Peer reviewed |
 | Kowalczyk, W., & Vlassis, N. (2005). Newscast EM. In Advances in Neural Information Processing Systems 17 (pp. 713-720). MIT Press. Peer reviewed |
 | Kok, J. R., Spaan, M. T. J., & Vlassis, N. (2005). Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems, 50 (2-3), 99-114. doi:10.1016/j.robot.2004.08.003 Peer Reviewed verified by ORBi |
 | Spaan, M. T. J., & Vlassis, N. (2004). A point-based POMDP algorithm for robot planning. In Proc. IEEE Int. Conf. on Robotics and Automation, New Orleans, Louisiana (pp. 2399-2404). Peer reviewed |
Diplaros, A., Gevers, T., & Vlassis, N. (2004). Skin detection using the EM algorithm with spatial constraints. In Proc. Int. Conf. on Systems, Man and Cybernetics. Peer reviewed |
 | Vlassis, N., Elhorst, R., & Kok, J. R. (2004). Anytime Algorithms for Multiagent Decision Making Using Coordination Graphs. In Proceedings of the International Conference on Systems, Man and Cybernetics (pp. 953-957). New York, United States - New York: Institute of Electronics Engineers. |
 | Kok, J. R., & Vlassis, N. (2004). Sparse Cooperative Q-learning. In Proc. 21st Int. Conf. on Machine Learning, Banff, Canada (pp. 481-488). Peer reviewed |
 | Verbeek, J. J., Roweis, S. T., & Vlassis, N. (2004). Non-linear CCA and PCA by Alignment of Local Models. In Advances in Neural Information Processing Systems 16 (pp. 297-304). San Mateo, United States - California: Morgan Kaufmann Publishers. Peer reviewed |
Kröse, B., Bunschoten, R., ten Hagen, S., Terwijn, B., & Vlassis, N. (2004). Household robots look and learn. IEEE Robotics and Automation Magazine, 11 (4), 45-52. doi:10.1109/MRA.2004.1371608 Peer Reviewed verified by ORBi |
 | Verbeek, J. J., Vlassis, N., & Kröse, B. (2003). Efficient Greedy Learning of Gaussian Mixture Models. Neural Computation, 15 (2), 469-485. doi:10.1162/089976603762553004 Peer Reviewed verified by ORBi |
 | Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36 (2), 451-461. doi:10.1016/S0031-3203(02)00060-2 Peer Reviewed verified by ORBi |
 | Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2003). Self-Organization by Optimizing Free-Energy. In Proc. of European Symposium on Artificial Neural Networks (pp. 125-130). Peer reviewed |
Kok, J. R., Spaan, M. T. J., & Vlassis, N. (2003). Multi-Robot Decision Making Using Coordination Graphs. In Proceedings of the International Conference on Advanced Robotics (ICAR) (pp. 1124-1129). Peer reviewed |
 | Vlassis, N., & Likas, A. (2002). A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters, 15 (1), 77-87. doi:10.1023/A:1013844811137 Peer reviewed |
 | Vlassis, N., Terwijn, B., & Krose, B. (2002). Auxiliary particle filter robot localization from high-dimensional sensor observations. In IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS I-IV, PROCEEDINGS (pp. 7-12). Peer reviewed |
 | Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). A k-segments algorithm for finding principal curves. Pattern Recognition Letters, 23 (8), 1009-1017. doi:10.1016/S0167-8655(02)00032-6 Peer Reviewed verified by ORBi |
 | Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2002). Coordinating Principal Component Analyzers. In Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain (pp. 914-919). Berlin, Germany: Springer. Peer reviewed |
| Vlassis, N., Motomura, Y., & Kröse, B. (2002). Supervised dimension reduction of intrinsically low-dimensional data. Neural Computation, 14 (1), 191 - 215. doi:10.1162/089976602753284491 Peer Reviewed verified by ORBi |
Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks. In Proceedings of the Tenth European Symposium on Artificial Neural Networks (pp. 193-198). Peer reviewed |
Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). Fast nonlinear dimensionality reduction with topology representing networks. In Proc. Europ. Symp. on Artificial Neural Networks. Peer reviewed |
Vlassis, N., Motomura, Y., Hara, I., Asoh, H., & Matsui, T. (2001). Edge-based features from omnidirectional images for robot localization. In Proc. IEEE Int. Conf. on Robotics and Automation. Peer reviewed |
Vlassis, N. (2001). Fast score function estimation with application in ICA. In Proc. Int. Conf. on Artificial Neural Networks. |
 | Vlassis, N., & Motomura, Y. (2001). Efficient source adaptivity in independent component analysis. IEEE Transactions on Neural Networks, 12 (3), 559-566. doi:10.1109/72.925558 Peer Reviewed verified by ORBi |
 | Vlassis, N., Bunschoten, R., & Kröse, B. (2001). Learning Task-Relevant Features From Robot Data. In IEEE International Conference on Robotics and Automation, 2001. Proceedings 2001 ICRA (pp. 499 - 504). Peer reviewed |
Verbeek, J. J., Vlassis, N., & Krose, B. (2001). A soft k-segments algorithm for principal curves. In ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS (pp. 450-456). Peer reviewed |
Asoh, H., Vlassis, N., Motomura, Y., Asano, F., Hara, I., Hayamizu, S., Itou, K., Kurita, T., Matsui, T., Bunschoten, R., & Kröse, B. (2001). Jijo-2: An office robot that communicates and learns. IEEE Intelligent Systems, 16 (5), 46-55. doi:10.1109/5254.956081 Peer Reviewed verified by ORBi |
 | Krose, B. J. A., Vlassis, N., Bunschoten, R., & Motomura, Y. (2001). A probabilistic model for appearance-based robot localization. Image and Vision Computing, 19 (6), 381-391. doi:10.1016/S0262-8856(00)00086-X Peer Reviewed verified by ORBi |
 | Vlassis, N., Motomura, Y., & Krose, B. (2000). Supervised linear feature extraction for mobile robot localization. In Proc. IEEE Int. Conf. on Robotics and Automation (pp. 2979 - 2984). Peer reviewed |
 | Vlassis, N., & Krose, B. (1999). Robot environment modeling via principal component regression. In Proc. of Intelligent Robots and Systems, International Conference on (pp. 677 - 682). Peer reviewed |
 | Vlassis, N., & Kröse, B. (1999). Mixture Conditional Density Estimation with the EM Algorithm. In Proc. 9th Int. Conf. on Artificial Neural Networks. Peer reviewed |
Vlassis, N., Motomura, Y., & Kröse, B. (1999). An information-theoretic localization criterion for robot map building. In Proc. ACAI'99, Int. Conf. on Machine Learning and Applications. Peer reviewed |
Kröse, B., Bunschoten, R., Vlassis, N., & Motomura, Y. (1999). Appearance-Based Robot Localization. In Proc. IJCAI'99, 16th Int. Joint Conf. on Artificial Intelligence, ROB-2 Workshop. Peer reviewed |
Vlassis, N., Papakonstantinou, G., & Tsanakas, P. (1999). Mixture density estimation based on Maximum Likelihood and test statistics. Neural Processing Letters, 9 (1), 63-76. doi:10.1023/A:1018624029058 Peer reviewed |
Vlassis, N., & Likas, A. (1999). A kurtosis-based dynamic approach to Gaussian mixture modeling. IEEE Transactions on Systems, Man and Cybernetics. Part A, Systems and Humans, 29 (4), 393-399. doi:10.1109/3468.769758 Peer reviewed |
Vlassis, N., Papakonstantinou, G., & Tsanakas, P. (1998). Dynamic Sensory Probabilistic Maps for Mobile Robot Localization. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. Peer reviewed |
Vlassis, N., & Tsanakas, P. (1998). A Sensory Uncertainty Field Model for Unknown and Non-stationary Mobile Robot Environments. In Proc. IEEE Int. Conf. on Robotics and Automation. Peer reviewed |
Vlassis, N., Dimopoulos, A., & Papakonstantinou, G. (1997). The Probabilistic Growing Cell Structures Algorithm. In Proc. Int. Conf. on Artificial Neural Networks. Peer reviewed |
Vlassis, N., Sgouros, N. M., Efthivoulidis, G., Papakonstantinou, G., & Tsanakas, P. (1996). Global Path Planning for Autonomous Qualitative Navigation. In Proc. 8th IEEE Int. Conf. on Tools with AI. Peer reviewed |
Efthivoulidis, G., Vlassis, N., Tsanakas, P., & Papakonstantinou, G. (1996). An Experiment for Truly Parallel Logic Programming. Journal of Intelligent and Robotic Systems, 16 (2), 169-184. doi:10.1007/BF00449704 Peer reviewed |