World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
14034
World Ranking
6048
National Ranking
2728

Overview

Nikos Vlassis is a researcher affiliated with Adobe Systems in the United States. Their work spans the fields of Computer Science and Mathematics, with key interests focused on Artificial Intelligence, Statistics and Probability, as well as Management Science and Operations Research. Additional involvement includes research in Electrical and Electronic Engineering and Computer Vision and Pattern Recognition.

Their research topics cover a variety of advanced and technical areas including:

  • Advanced Bandit Algorithms Research
  • Smart Grid Energy Management
  • Reinforcement Learning in Robotics
  • Advanced Causal Inference Techniques
  • Statistical Methods and Inference
  • Recommender Systems and Techniques
  • Statistical Methods in Clinical Trials

Vlassis has contributed to several papers published primarily in arXiv (Cornell University) and the Proceedings of the AAAI Conference on Artificial Intelligence. Selected recent publications include:

  • Control Variates for Slate Off-Policy Evaluation, 2021, arXiv (Cornell University)
  • On Proximal Causal Learning with Many Hidden Confounders, 2020, arXiv (Cornell University)
  • Off-Policy Evaluation of Slate Policies under Bayes Risk, 2021, arXiv (Cornell University)
  • Sequential causal inference in a single world of connected units, 2021, arXiv (Cornell University)
  • Distributional Off-Policy Evaluation for Slate Recommendations, 2024, Proceedings of the AAAI Conference on Artificial Intelligence

Frequently collaborating with other researchers, Vlassis has coauthored works with Shreyas Chaudhari, David Arbour, Georgios Theocharous, Ashok Chandrashekar, and Fernando Amat Gil.

Best Publications

  • The global k-means clustering algorithm

    Aristidis Likas;Nikos A. Vlassis;Jakob J. Verbeek

  • Perseus: randomized point-based value iteration for POMDPs

    Matthijs T. J. Spaan;Nikos Vlassis

  • Efficient greedy learning of Gaussian mixture models

    J. J. Verbeek;N. Vlassis;B. Kröse

  • Optimal and approximate Q-value functions for decentralized POMDPs

    Frans A. Oliehoek;Matthijs T. J. Spaan;Nikos Vlassis

  • VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data.

    Cedric Christian Laczny;Tomasz Sternal;Valentin Plugaru;Piotr Gawron

  • A Greedy EM Algorithm for Gaussian Mixture Learning

    Nikos Vlassis;Aristidis Likas

  • Collaborative Multiagent Reinforcement Learning by Payoff Propagation

    Jelle R. Kok;Nikos Vlassis

  • An analytic solution to discrete Bayesian reinforcement learning

    Pascal Poupart;Nikos Vlassis;Jesse Hoey;Kevin Regan

  • Point-Based Value Iteration for Continuous POMDPs

    Josep M. Porta;Nikos Vlassis;Matthijs T.J. Spaan;Pascal Poupart

  • A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence

    Nikos Vlassis

  • Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs

    Lior Kuyer;Shimon Whiteson;Bram Bakker;Nikos Vlassis

  • Fast reconstruction of compact context-specific metabolic network models.

    Nikos Vlassis;Maria Pires Pacheco;Thomas Sauter

  • A probabilistic model for appearance-based robot localization

    Ben J. A. Kröse;Nikos A. Vlassis;Roland Bunschoten;Yoichi Motomura

  • Accelerated Variational Dirichlet Process Mixtures

    Kenichi Kurihara;Max Welling;Nikos A. Vlassis

  • A k-segments algorithm for finding principal curves

    J. J. Verbeek;N. Vlassis;B. Kröse

  • Sparse cooperative Q-learning

    Jelle R. Kok;Nikos Vlassis

  • Jijo-2: an office robot that communicates and learns

    H. Asoh;Y. Motomura;F. Asano;I. Hara

  • A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation

    A. Diplaros;N. Vlassis;T. Gevers

  • Auxiliary particle filter robot localization from high-dimensional sensor observations

    N. Vlassis;B. Terwijn;B. Krose

  • Exploiting locality of interaction in factored Dec-POMDPs

    Frans A. Oliehoek;Matthijs T. J. Spaan;Shimon Whiteson;Nikos Vlassis

Frequent Co-Authors

Ben Kröse
Ben Kröse Amsterdam University of Applied Sciences
Jakob Verbeek
Jakob Verbeek Facebook AI Research (FAIR) in Paris
Paul Wilmes
Paul Wilmes University of Luxembourg
Frans C. A. Groen
Frans C. A. Groen University of Amsterdam
Pascal Poupart
Pascal Poupart University of Waterloo
Michael L. Littman
Michael L. Littman Brown University
Aristidis Likas
Aristidis Likas University of Ioannina
Ines Thiele
Ines Thiele University of Galway
Ronan M. T. Fleming
Ronan M. T. Fleming Leiden University
David Barber
David Barber University College London

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