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ORCIDNikos Vlassis
Nikos A. Vlassis
Person information
- affiliation (2014-2017, since 2022): Adobe Research, San Jose, CA, USA
- affiliation (2017-2022): Netflix Research, Los Gatos, CA, USA
- affiliation (2010-2014): University of Luxembourg, Centre for Systems Biomedicine, Luxembourg
- affiliation (2007-2010): Technical University of Crete, Greece
- affiliation (2001-2007): University of Amsterdam, The Netherlands
- affiliation (PhD 1998): National Technical University of Athens, Greece
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2020 – today
- 2024
[c63]Shreyas Chaudhari, David Arbour, Georgios Theocharous, Nikos Vlassis:
Distributional Off-Policy Evaluation for Slate Recommendations. AAAI 2024: 8265-8273- 2023
- [i19]Ashish Singh, Prateek Agarwal, Zixuan Huang, Arpita Singh, Tong Yu, Sungchul Kim, Victor S. Bursztyn, Nikos Vlassis, Ryan A. Rossi:
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback. CoRR abs/2307.10867 (2023) - [i18]Shreyas Chaudhari, David Arbour, Georgios Theocharous, Nikos Vlassis:
Distributional Off-Policy Evaluation for Slate Recommendations. CoRR abs/2308.14165 (2023) - 2022
- [i17]Dawen Liang, Nikos Vlassis:
Local Policy Improvement for Recommender Systems. CoRR abs/2212.11431 (2022) - 2021
- [c62]Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus:
Control Variates for Slate Off-Policy Evaluation. NeurIPS 2021: 3667-3679 - [i16]Nikos Vlassis, Fernando Amat Gil, Ashok Chandrashekar:
Off-Policy Evaluation of Slate Policies under Bayes Risk. CoRR abs/2101.02553 (2021) - [i15]Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus:
Control Variates for Slate Off-Policy Evaluation. CoRR abs/2106.07914 (2021)
2010 – 2019
- 2019
- [c61]Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis:
Optimizing over a Restricted Policy Class in MDPs. AISTATS 2019: 3042-3050 - [c60]Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan:
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. ICML 2019: 654-663 - [c59]Nikos Vlassis, Aurélien Bibaut, Maria Dimakopoulou, Tony Jebara:
On the Design of Estimators for Bandit Off-Policy Evaluation. ICML 2019: 6468-6476 - [c58]Maria Dimakopoulou, Nikos Vlassis, Tony Jebara:
Marginal Posterior Sampling for Slate Bandits. IJCAI 2019: 2223-2229 - [i14]Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan:
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. CoRR abs/1912.06292 (2019) - 2018
- [c57]Frits de Nijs, Georgios Theocharous, Nikos Vlassis, Mathijs Michiel de Weerdt, Matthijs T. J. Spaan:
Capacity-aware Sequential Recommendations. AAMAS 2018: 416-424 - [c56]Georgios Theocharous, Zheng Wen, Yasin Abbasi, Nikos Vlassis:
Scalar Posterior Sampling with Applications. NeurIPS 2018: 7696-7704 - [i13]Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis:
Optimizing over a Restricted Policy Class in Markov Decision Processes. CoRR abs/1802.09646 (2018) - 2017
- [c55]Dimitris Achlioptas, Fotis Iliopoulos, Nikos Vlassis:
Stochastic Control via Entropy Compression. ICALP 2017: 83:1-83:13 - [c54]Georgios Theocharous, Nikos Vlassis, Zheng Wen:
An Interactive Points of Interest Guidance System. IUI Companion 2017: 49-52 - [c53]Shi Zong, Branislav Kveton, Shlomo Berkovsky
, Azin Ashkan, Nikos Vlassis, Zheng Wen:
Does Weather Matter?: Causal Analysis of TV Logs. WWW (Companion Volume) 2017: 883-884 - [i12]Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Nikos Vlassis, Zheng Wen:
Does Weather Matter? Causal Analysis of TV Logs. CoRR abs/1701.08716 (2017) - [i11]Georgios Theocharous, Zheng Wen, Yasin Abbasi-Yadkori, Nikos Vlassis:
Posterior Sampling for Large Scale Reinforcement Learning. CoRR abs/1711.07979 (2017) - 2016
- [c52]Nicolò Colombo, Nikos Vlassis:
Tensor Decomposition via Joint Matrix Schur Decomposition. ICML 2016: 2820-2828 - [c51]Sheng Li, Nikos Vlassis, Jaya Kawale, Yun Fu:
Matching via Dimensionality Reduction for Estimation of Treatment Effects in Digital Marketing Campaigns. IJCAI 2016: 3768-3774 - [c50]Suvash Sedhain, Hung Bui, Jaya Kawale, Nikos Vlassis, Branislav Kveton, Aditya Krishna Menon, Trung Bui, Scott Sanner:
Practical Linear Models for Large-Scale One-Class Collaborative Filtering. IJCAI 2016: 3854-3860 - [c49]Florian Bernard, Nikos Vlassis, Peter Gemmar, Andreas Husch, Johan Thunberg, Jorge M. Gonçalves, Frank Hertel:
Fast correspondences for statistical shape models of brain structures. Image Processing 2016: 97840R - [c48]Nicolò Colombo, Nikos Vlassis:
A posteriori error bounds for joint matrix decomposition problems. NIPS 2016: 4943-4950 - [i10]Nicolò Colombo, Nikos Vlassis:
Approximate Joint Matrix Triangularization. CoRR abs/1607.00514 (2016) - [i9]Dimitris Achlioptas, Fotis Iliopoulos, Nikos Vlassis:
Stochastic Control via Entropy Compression. CoRR abs/1607.06494 (2016) - [i8]Ehsan Amid, Nikos Vlassis, Manfred K. Warmuth:
t-Exponential Triplet Embedding. CoRR abs/1611.09957 (2016) - 2015
- [j29]Nicolò Colombo, Nikos Vlassis:
FastMotif: spectral sequence motif discovery. Bioinform. 31(16): 2623-2631 (2015) - [c47]Luis Salamanca, Nikos Vlassis, Nico Diederich, Florian Bernard, Alexander Skupin:
Improved Parkinson's Disease Classification from Diffusion MRI Data by Fisher Vector Descriptors. MICCAI (2) 2015: 119-126 - [c46]Nicolò Colombo, Nikos Vlassis:
Stable Spectral Learning Based on Schur Decomposition. UAI 2015: 220-227 - 2014
- [j28]Ines Thiele, Nikos Vlassis, Ronan M. T. Fleming:
fastGapFill: efficient gap filling in metabolic networks. Bioinform. 30(17): 2529-2531 (2014) - [j27]Nikos Vlassis, Maria Pires Pacheco, Thomas Sauter:
Fast Reconstruction of Compact Context-Specific Metabolic Network Models. PLoS Comput. Biol. 10(1) (2014) - [j26]Nikos Vlassis, Raphaël M. Jungers:
Polytopic uncertainty for linear systems: New and old complexity results. Syst. Control. Lett. 67: 9-13 (2014) - [i7]Nicolò Colombo, Nikos Vlassis:
Spectral Sequence Motif Discovery. CoRR abs/1407.6125 (2014) - 2013
- [i6]Nikos Vlassis, Maria Pires Pacheco, Thomas Sauter:
Fast Reconstruction of Compact Context-Specific Metabolic Network Models. CoRR abs/1304.7992 (2013) - [i5]Nikos Vlassis, Raphaël M. Jungers:
Polytopic uncertainty for linear systems: New and old complexity results. CoRR abs/1310.1930 (2013) - 2012
- [j25]Nikos Vlassis, Michael L. Littman, David Barber:
On the Computational Complexity of Stochastic Controller Optimization in POMDPs. ACM Trans. Comput. Theory 4(4): 12:1-12:8 (2012) - [p1]Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor, Pascal Poupart:
Bayesian Reinforcement Learning. Reinforcement Learning 2012: 359-386 - [i4]Nikos Vlassis:
NP-hardness of polytope M-matrix testing and related problems. CoRR abs/1206.2059 (2012) - 2011
- [i3]Nikos Vlassis, Michael L. Littman, David Barber:
On the computational complexity of stochastic controller optimization in POMDPs. CoRR abs/1107.3090 (2011) - [i2]Matthijs T. J. Spaan, Nikos Vlassis:
Perseus: Randomized Point-based Value Iteration for POMDPs. CoRR abs/1109.2145 (2011) - [i1]Frans A. Oliehoek, Matthijs T. J. Spaan, Nikos Vlassis:
Optimal and Approximate Q-value Functions for Decentralized POMDPs. CoRR abs/1111.0062 (2011)
2000 – 2009
- 2009
- [b1]Nikos Vlassis:
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009, ISBN 978-3-031-00415-5 - [j24]Nikos Vlassis, Marc Toussaint, Georgios Kontes, Savas Piperidis:
Learning model-free robot control by a Monte Carlo EM algorithm. Auton. Robots 27(2): 123-130 (2009) - [c45]Nikos Vlassis, Marc Toussaint:
Model-free reinforcement learning as mixture learning. ICML 2009: 1081-1088 - 2008
- [j23]Frans A. Oliehoek, Julian F. P. Kooij, Nikos Vlassis:
The Cross-Entropy Method for Policy Search in Decentralized POMDPs. Informatica (Slovenia) 32(4): 341-357 (2008) - [j22]Frans A. Oliehoek, Matthijs T. J. Spaan, Nikos Vlassis:
Optimal and Approximate Q-value Functions for Decentralized POMDPs. J. Artif. Intell. Res. 32: 289-353 (2008) - [c44]Matthijs T. J. Spaan, Frans A. Oliehoek, Nikos Vlassis:
Multiagent Planning Under Uncertainty with Stochastic Communication Delays. ICAPS 2008: 338-345 - [c43]Frans A. Oliehoek, Matthijs T. J. Spaan, Shimon Whiteson, Nikos Vlassis:
Exploiting locality of interaction in factored Dec-POMDPs. AAMAS (1) 2008: 517-524 - [c42]Pascal Poupart, Nikos Vlassis:
Model-based Bayesian Reinforcement Learning in Partially Observable Domains. ISAIM 2008 - [c41]Lior Kuyer, Shimon Whiteson, Bram Bakker, Nikos Vlassis:
Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs. ECML/PKDD (1) 2008: 656-671 - 2007
- [j21]Aristeidis Diplaros, Nikos Vlassis, Theo Gevers:
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation. IEEE Trans. Neural Networks 18(3): 798-808 (2007) - [c40]Frans A. Oliehoek, Nikos Vlassis:
Q-value Heuristics for Approximate Solutions of Dec-POMDPs. AAAI Spring Symposium: Game Theoretic and Decision Theoretic Agents 2007: 31-37 - [c39]Frans A. Oliehoek, Nikos Vlassis:
Q-value functions for decentralized POMDPs. AAMAS 2007: 220 - [c38]Nikos Vlassis:
Distributed Decision Making for Robot Teams. IDC 2007: 35-40 - [c37]Frans A. Oliehoek, Julian F. P. Kooij, Nikos Vlassis:
A Cross-Entropy Approach to Solving Dec-POMDPs. IDC 2007: 145-154 - 2006
- [j20]Jakob J. Verbeek, Jan Nunnink, Nikos Vlassis:
Accelerated EM-based clustering of large data sets. Data Min. Knowl. Discov. 13(3): 291-307 (2006) - [j19]Jelle R. Kok, Nikos Vlassis:
Collaborative Multiagent Reinforcement Learning by Payoff Propagation. J. Mach. Learn. Res. 7: 1789-1828 (2006) - [j18]Josep M. Porta, Nikos Vlassis, Matthijs T. J. Spaan, Pascal Poupart:
Point-Based Value Iteration for Continuous POMDPs. J. Mach. Learn. Res. 7: 2329-2367 (2006) - [j17]Jakob J. Verbeek, Nikos Vlassis:
Gaussian fields for semi-supervised regression and correspondence learning. Pattern Recognit. 39(10): 1864-1875 (2006) - [j16]Nikos Vlassis, Geoffrey J. Gordon, Joelle Pineau:
Planning under uncertainty in robotics. Robotics Auton. Syst. 54(11): 885-886 (2006) - [c36]Michael R. James, Ton Wessling, Nikos Vlassis:
Improving Approximate Value Iteration Using Memories and Predictive State Representations. AAAI 2006: 375-380 - [c35]Matthijs T. J. Spaan, Geoffrey J. Gordon, Nikos Vlassis:
Decentralized planning under uncertainty for teams of communicating agents. AAMAS 2006: 249-256 - [c34]Frans A. Oliehoek, Edwin D. de Jong, Nikos Vlassis:
The parallel Nash Memory for asymmetric games. GECCO 2006: 337-344 - [c33]Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan:
An analytic solution to discrete Bayesian reinforcement learning. ICML 2006: 697-704 - [c32]Kenichi Kurihara, Max Welling, Nikos Vlassis:
Accelerated Variational Dirichlet Process Mixtures. NIPS 2006: 761-768 - 2005
- [j15]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
Self-organizing mixture models. Neurocomputing 63: 99-123 (2005) - [j14]Matthijs T. J. Spaan, Nikos Vlassis:
Perseus: Randomized Point-based Value Iteration for POMDPs. J. Artif. Intell. Res. 24: 195-220 (2005) - [j13]Jelle R. Kok, Matthijs T. J. Spaan, Nikos Vlassis:
Non-communicative multi-robot coordination in dynamic environments. Robotics Auton. Syst. 50(2-3): 99-114 (2005) - [c31]Frans A. Oliehoek, Nikos Vlassis, Edwin D. de Jong:
Coevolutionary Nash in poker games. BNAIC 2005: 188-193 - [c30]Jelle R. Kok, Nikos Vlassis:
Using the Max-Plus Algorithm for Multiagent Decision Making in Coordination Graphs. BNAIC 2005: 359-360 - [c29]Josep M. Porta, Matthijs T. J. Spaan, Nikos Vlassis:
Robot Planning in Partially Observable Continuous Domains. BNAIC 2005: 375-376 - [c28]Jelle R. Kok, Pieter Jan't Hoen, Bram Bakker, Nikos Vlassis:
Utile Coordination: Learning Interdependencies Among Cooperative Agents. CIG 2005 - [c27]Matthijs T. J. Spaan, Nikos Vlassis:
Planning with Continuous Actions in Partially Observable Environments. ICRA 2005: 3458-3463 - [c26]Nikos Vlassis, Yiannis Sfakianakis, Wojtek Kowalczyk:
Gossip-Based Greedy Gaussian Mixture Learning. Panhellenic Conference on Informatics 2005: 349-359 - [c25]Jelle R. Kok, Nikos Vlassis:
Using the Max-Plus Algorithm for Multiagent Decision Making in Coordination Graphs. RoboCup 2005: 1-12 - [c24]Josep M. Porta, Matthijs T. J. Spaan, Nikos Vlassis:
Robot Planning in Partially Observable Continuous Domains. Robotics: Science and Systems 2005: 217-224 - 2004
- [j12]Ben J. A. Kröse, Roland Bunschoten, Stephan ten Hagen, Bas Terwijn, Nikos Vlassis:
Household robots look and learn: environment modeling and localization from an omnidirectional vision system. IEEE Robotics Autom. Mag. 11(4): 45-52 (2004) - [c23]Jelle R. Kok, Nikos Vlassis:
Sparse cooperative Q-learning. ICML 2004 - [c22]Matthijs T. J. Spaan, Nikos Vlassis:
A Point-based POMDP Algorithm for Robot Planning. ICRA 2004: 2399-2404 - [c21]Wojtek Kowalczyk, Nikos Vlassis:
Newscast EM. NIPS 2004: 713-720 - [c20]Nikos Vlassis, Reinoud Elhorst, Jelle R. Kok:
Anytime algorithms for multiagent decision making using coordination graphs. SMC (1) 2004: 953-957 - [c19]Aristeidis Diplaros, Theo Gevers, Nikos Vlassis:
Skin detection using the EM algorithm with spatial constraints. SMC (4) 2004: 3071-3075 - 2003
- [j11]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
Efficient Greedy Learning of Gaussian Mixture Models. Neural Comput. 15(2): 469-485 (2003) - [j10]Aristidis Likas, Nikos Vlassis, Jakob J. Verbeek:
The global k-means clustering algorithm. Pattern Recognit. 36(2): 451-461 (2003) - [c18]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
Self-Organization by Optimizing Free-Energy. ESANN 2003: 125-130 - [c17]Jakob J. Verbeek, Sam T. Roweis, Nikos Vlassis:
Non-linear CCA and PCA by Alignment of Local Models. NIPS 2003: 297-304 - 2002
- [j9]Nikos Vlassis, Yoichi Motomura, Ben J. A. Kröse:
Supervised Dimension Reduction of Intrinsically Low-Dimensional Data. Neural Comput. 14(1): 191-215 (2002) - [j8]Nikos Vlassis, Aristidis Likas:
A Greedy EM Algorithm for Gaussian Mixture Learning. Neural Process. Lett. 15(1): 77-87 (2002) - [j7]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
A k-segments algorithm for finding principal curves. Pattern Recognit. Lett. 23(8): 1009-1017 (2002) - [c16]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
Fast nonlinear dimensionality reduction with topology representing networks. ESANN 2002: 193-198 - [c15]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
Coordinating Principal Component Analyzers. ICANN 2002: 914-919 - [c14]Nikos Vlassis, Bas Terwijn, Ben J. A. Kröse:
Auxiliary Particle Filter Robot Localization from High-Dimensional Sensor Observations. ICRA 2002: 7-12 - [c13]Jelle R. Kok, Remco C. de Boer, Nikos Vlassis, Frans C. A. Groen:
Towards an Optimal Scoring Policy for Simulated Soccer Agents. RoboCup 2002: 296-303 - 2001
- [j6]Hideki Asoh, Nikos Vlassis, Yoichi Motomura, Futoshi Asano, Isao Hara, Satoru Hayamizu, Katsunobu Itou, Takio Kurita, Toshihiro Matsui, Roland Bunschoten, Ben J. A. Kröse:
Jijo-2: An Office Robot that Communicates and Learns. IEEE Intell. Syst. 16(5): 46-55 (2001) - [j5]Ben J. A. Kröse, Nikos Vlassis, Roland Bunschoten, Yoichi Motomura:
A probabilistic model for appearance-based robot localization. Image Vis. Comput. 19(6): 381-391 (2001) - [j4]Nikos Vlassis, Yoichi Motomura:
Efficient source adaptivity in independent component analysis. IEEE Trans. Neural Networks 12(3): 559-566 (2001) - [c12]Jakob J. Verbeek, Nikos Vlassis, Ben J. A. Kröse:
A Soft k-Segments Algorithm for Principal Curves. ICANN 2001: 450-456 - [c11]Nikos Vlassis:
Fast Score Function Estimation with Application in ICA. ICANN 2001: 541-546 - [c10]Nikos Vlassis, Roland Bunschoten, Ben J. A. Kröse:
Learning Task-relevant Features from Robot Data. ICRA 2001: 499-504 - [c9]Nikos Vlassis, Yoichi Motomura, Isao Hara, Hideki Asoh:
Edge-based Features from Omnidirectional Images for Robot Localization. ICRA 2001: 1579-1584 - 2000
- [c8]Ben J. A. Kröse, Nikos Vlassis, Roland Bunschoten:
Omnidirectional Vision for Appearance-Based Robot Localization. Sensor Based Intelligent Robots 2000: 39-50 - [c7]Nikos Vlassis, Yoichi Motomura, Ben J. A. Kröse:
Supervised Linear Feature Extraction for Mobile Robot Localization. ICRA 2000: 2979-2984
1990 – 1999
- 1999
- [j3]Nikos A. Vlassis, George K. Papakonstantinou, Panayotis Tsanakas:
Mixture Density Estimation Based on Maximum Likelihood and Sequential Test Statistics. Neural Process. Lett. 9(1): 63-76 (1999) - [j2]Nikos Vlassis, Aristidis Likas:
A kurtosis-based dynamic approach to Gaussian mixture modeling. IEEE Trans. Syst. Man Cybern. Part A 29(4): 393-399 (1999) - [c6]Nikos Vlassis, Ben J. A. Kröse:
Robot environment modeling via principal component regression. IROS 1999: 677-682 - 1998
- [c5]Nikos A. Vlassis, Konstantinos Blekas, George K. Papakonstantinou, Andreas Stafylopatis:
A vector quantization schema for non-stationary signal distributions based on ML estimation of mixture densities. EUSIPCO 1998: 1-4 - [c4]Nikos A. Vlassis, Panayotis Tsanakas:
A Sensory Uncertainty Field Model for Unknown and Non-Stationary Mobile Robot Environments. ICRA 1998: 363-368 - [c3]Nikos A. Vlassis, George K. Papakonstantinou, Panayotis Tsanakas:
Dynamic sensory probabilistic maps for mobile robot localization. IROS 1998: 718-723 - 1997
- [c2]Nikos A. Vlassis, Apostolos Dimopoulos, George K. Papakonstantinou:
The Probabilistic Growing Cell Structures Algorithm. ICANN 1997: 649-654 - 1996
- [j1]George K. Efthivoulidis, Nikos Vlassis, Panayotis Tsanakas, George K. Papakonstantinou:
An experiment for truly parallel logic programming. J. Intell. Robotic Syst. 16(2): 169-184 (1996) - [c1]Nikos A. Vlassis, Nikitas M. Sgouros, G. Efthivoulidis, George K. Papakonstantinou, Panayotis Tsanakas:
Global Path Planning for Autonomous Qualitative Navigation. ICTAI 1996: 354-359