Causal inference / Off-policy evaluation

• N Vlassis, A Chandrashekar, FA Gil, N Kallus. Control variates for slate off-policy evaluation. NeurIPS, 2021.

• N Vlassis, P Hebda, S McBride, A Noulas. On proximal causal learning with many hidden confounders. arXiv, 2020.

• A Bibaut, I Malenica, N Vlassis, M Van Der Laan. More efficient off-policy evaluation through regularized targeted learning. ICML, 2019

• S. Li, N. Vlassis, J. Kawale and Y. Fu. Matching via dimensionality reduction for estimation of treatment effects in digital marketing campaigns. IJCAI, 2016.

Markov decision processes / POMDPs

• E. Banijamali, Y. Abbasi-Yadkori, M. Ghavamzadeh, N. Vlassis. Optimizing over a restricted policy class in MDPs. AISTATS, 2019.

• F. de Nijs, G. Theocharous, N. Vlassis, M.M. de Weerdt, M.T.J. Spaan. Capacity-aware sequential recommendations. AAMAS, 2018

• N. Vlassis, M.L. Littman, and D. Barber. On the computational complexity of stochastic controller optimization in POMDPs. ACM Transactions on Computation Theory 4:4, 2012.

• N. Vlassis, M.L. Littman, and D. Barber. Stochastic POMDP controllers: How easy to optimize? In Proc. 10th European Workshop on Reinforcement Learning, Edinburgh, Scotland, 2012.

• J.M. Porta, N. Vlassis, M.T.J. Spaan, and P. Poupart. Point-based value iteration for continuous POMDPs. Journal of Machine Learning Research, 7:2329-2367, 2006.

• M.T.J. Spaan and N. Vlassis. Planning with continuous actions in partially observable environments. In Proc. IEEE Int. Conf. on Robotics and Automation, Barcelona, Spain, April 2005.

• M.T.J. Spaan and N. Vlassis. Perseus: Randomized point-based value iteration for POMDPs. Journal of Artificial Intelligence Research, 24:195-220, 2005.

• N. Vlassis and M.T.J. Spaan. A fast point-based algorithm for POMDPs. In Proc. Annual Machine Learning Conf. of Belgium and the Netherlands, 2004.

Reinforcement learning

• G. Theocharous, Z. Wen, Y. Abbasi-Yadkori, and N. Vlassis. Scalar posterior sampling with applications. NIPS, 2018.

• N. Vlassis, M. Ghavamzadeh, S. Mannor, and P. Poupart. Bayesian reinforcement learning. In Reinforcement Learning: State of the Art. M. Wiering and M. van Otterlo (eds.), Springer, 2012.

• N. Vlassis and M. Toussaint. Model-free reinforcement learning as mixture learning. In Proc. Int. Conf. on Machine Learning, Montreal, Canada, 2009.

• P. Poupart and N. Vlassis. Model-based Bayesian reinforcement learning in partially observable domains. In Proc. Int. Symp. on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, USA, 2008.

• L. Kuyer, S. Whiteson, B. Bakker, and N. Vlassis. Multiagent reinforcement learning for urban traffic control using coordination graphs. In Proc. 19th European Conf. on Machine Learning, Antwerp, Belgium, 2008.

• P. Poupart, N. Vlassis, J. Hoey, and K. Regan. An analytic solution to discrete Bayesian reinforcement learning. In Proc. Int. Conf. on Machine Learning, Pittsburgh, USA, 2006.

• J.R. Kok and N. Vlassis. Collaborative multiagent reinforcement learning by payoff propagation. Journal of Machine Learning Research, 7:1789-1828, 2006.

• J.R. Kok and N. Vlassis. Sparse cooperative Q-learning. In Proc. 21st Int. Conf. on Machine Learning, Banff, Canada, July 2004.

Multiagent systems / Decentralized control

• F.A. Oliehoek, M.T.J. Spaan, and N. Vlassis. Optimal and approximate Q-value functions for decentralized POMDPs. Journal of Artificial Intelligence Research, 32:289-353, 2008.

• M.T.J. Spaan, F.A. Oliehoek, and N. Vlassis. Multiagent planning under uncertainty with stochastic communication delays. In Proc. Int. Conf. on Automated Planning and Scheduling, Sydney, Australia, 2008.

• F.A. Oliehoek, M.T.J. Spaan, S. Whiteson, and N. Vlassis. Exploiting locality of interaction in factored Dec-POMDPs. In Proc. Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems, Estoril, Portugal, 2008.

• N. Vlassis. A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. Synthesis Lectures in Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2007.

• M.T.J. Spaan, G.J. Gordon, and N. Vlassis. Decentralized planning under uncertainty for teams of communicating agents. In Proc. Int. Joint Conf. on Autonomous Agents and Multiagent Systems, Hakodate, Japan, May 2006.

• J.R. Kok, P.J. 't Hoen, B. Bakker, and N. Vlassis. Utile coordination: Learning interdependencies among cooperative agents. In Proc. IEEE Symp. on Computational Intelligence and Games, Colchester, Essex, April 2005.

• J.R. Kok and N. Vlassis. Using the max-plus algorithm for multiagent decision making in coordination graphs. In Proc. RoboCup Int. Symposium, Osaka, Japan, July 2005. Best paper award.

Unsupervised learning / Clustering

• K. Kurihara, M. Welling, and N. Vlassis. Accelerated variational Dirichlet process mixtures. In Advances in Neural Information Processing Systems 19, 2007.

• J.J. Verbeek, J.R.J. Nunnink, and N. Vlassis. Accelerated EM-based clustering of large data sets. Data Mining and Knowledge Discovery, 13(3):291-307, 2006.

• J.J. Verbeek, N. Vlassis, and B. Kröse. Self-organizing mixture models. Neurocomputing, 63:99-123, 2005.

• W. Kowalczyk and N. Vlassis. Newscast EM. In Advances in Neural Information Processing Systems 17, 2005.

• J.J. Verbeek, S.T. Roweis, and N. Vlassis. Non-linear CCA and PCA by alignment of local models. In Advances in Neural Information Processing Systems 16, 2004.

• J.J. Verbeek, N. Vlassis, and B. Kröse. Efficient greedy learning of Gaussian mixture models. Neural Computation, 15(2):469-485, 2003.

• A. Likas, N. Vlassis, and J.J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36(2):451-461, 2003.

• J.J. Verbeek, N. Vlassis, and B.J.A. Kröse. A k-segments algorithm for finding principal curves. Pattern Recognition Letters, 23(8):1009-1017, 2002.

• N. Vlassis, Y. Motomura, and B.J.A. Kröse. Supervised dimension reduction of intrinsically low-dimensional data. Neural Computation, 14(1):191-215, 2002.

• N. Vlassis and A. Likas. A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters, 15(1):77-87, 2002.

• N. Vlassis and Y. Motomura. Efficient source adaptivity in independent component analysis. IEEE Trans. Neural Networks, 12(3):559-566, 2001.

Robotics

• N. Vlassis, M. Toussaint, G. Kontes, and S. Piperidis. Learning model-free robot control by a Monte Carlo EM algorithm. Autonomous Robots, 27(2):123-130, 2009.

• J.M. Porta, M.T.J. Spaan, and N. Vlassis. Robot planning in partially observable continuous domains. In Proc. Robotics: Science and Systems, MIT, Cambridge, MA, June 2005.

• J.R. Kok, M.T.J. Spaan, and N. Vlassis. Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems, 50(2-3):99-114, 2005.

• M.T.J. Spaan and N. Vlassis. A point-based POMDP algorithm for robot planning. In Proc. IEEE Int. Conf. on Robotics and Automation, New Orleans, Louisiana, April 2004.

• J.R. Kok, M.T.J. Spaan, and N. Vlassis. Multi-robot decision making using coordination graphs. In Proc. 11th Int. Conf. on Advanced Robotics, Coimbra, Portugal, June 2003.

• N. Vlassis, B. Terwijn, and B.J.A. Kröse. Auxiliary particle filter robot localization from high-dimensional sensor observations. In Proc. IEEE Int. Conf. on Robotics and Automation, Washington D.C., 2002.

• N. Vlassis, R. Bunschoten, and B.J.A. Kröse. Learning task-relevant features from robot data. In Proc. IEEE Int. Conf. on Robotics and Automation, pages 499-504, Seoul, Korea, May 2001.

• B.J.A. Kröse, N. Vlassis, R. Bunschoten, and Y. Motomura. A probabilistic model for appearance-based robot localization. Image and Vision Computing, 19(6):381-391, 2001.

• N. Vlassis, Y. Motomura, and B.J.A. Kröse. Supervised linear feature extraction for mobile robot localization. In Proc. IEEE Int. Conf. on Robotics and Automation, San Fransisco, CA, 2000.

Biology

• R.M.T. Fleming, N. Vlassis, I. Thiele, and M.A. Saunders. Conditions for duality between fluxes and concentrations in biochemical networks. Journal of Theoretical Biology, 409:1-10, 2016.

• N. Colombo and N. Vlassis. FastMotif: Spectral sequence motif discovery. Bioinformatics 31(16):2623-2631, 2015.

• N. Vlassis and E. Glaab. 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, 2015.

• C.C. Laczny, N. Pinel, N. Vlassis, and P. Wilmes. Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction. Scientific Reports 4:4516, 2014.

• N. Vlassis, M. Pires Pacheco, and T. Sauter. Fast reconstruction of compact context-specific metabolic network models. PLoS Computational Biology 10(1): e1003424, 2014.

Tensors

• N. Colombo and N. Vlassis. A posteriori error bounds for joint matrix decomposition problems. NIPS, 2016.

• N. Colombo and N. Vlassis. Tensor decomposition via joint matrix Schur decomposition. ICML, 2016.

Collaborative filtering

• S. Sedhain, H. Bui, J. Kawale, N. Vlassis, B. Kveton, A. Menon, T. Bui, and S. Sanner. Practical linear models for large-scale one-class collaborative filtering. IJCAI, 2016.

Control

• D. Achlioptas, F. Iliopoulos, and N. Vlassis. Stochastic control via entropy compression. ICALP, 2017.

• N. Vlassis, R.M. Jungers. Polytopic uncertainty for linear systems: New and old complexity results. Systems & Control Letters 67, 9-13, 2014.

Computer vision

• A. Diplaros, N. Vlassis, and T. Gevers. A spatially constrained generative model and an EM algorithm for image segmentation. IEEE Trans. Neural Networks, 18(3):798-808, 2007.