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Tamas Linder

Tamas Linder

D-Index & Metrics

Computer Science

D-Index
37
Citations
4956
World Ranking
10885
National Ranking
432

Research.com Recognitions

  • 2013 - IEEE Fellow For contributions to source coding and quantization

Overview

Tamas Linder is affiliated with Queen's University in Canada and has contributed extensively to research in the fields of Computer Science and Engineering. Their main research subfields include Electrical and Electronic Engineering, Computer Networks and Communications, Artificial Intelligence, Statistical and Nonlinear Physics, and Control and Systems Engineering.

The scientist's work spans several key topics, including:

  • Wireless Communication Security Techniques
  • Cooperative Communication and Network Coding
  • Statistical Methods and Inference
  • Game Theory and Applications
  • Stability and Control of Uncertain Systems
  • Advanced Bandit Algorithms Research
  • Advanced Wireless Network Optimization

Tamas Linder has published numerous papers, with notable recent works including:

  • Zero-Delay Lossy Coding of Linear Vector Markov Sources: Optimality of Stationary Codes and Near Optimality of Finite Memory Codes, 2021, IEEE Transactions on Information Theory
  • Reinforcement Learning for Near-Optimal Design of Zero-Delay Codes for Markov Sources, 2024, IEEE Transactions on Information Theory
  • Signaling Games for Log-Concave Distributions: Number of Bins and Properties of Equilibria, 2021, IEEE Transactions on Information Theory
  • Lossless Transformations and Excess Risk Bounds in Statistical Inference, 2023, Entropy
  • An Asymptotically Optimal Two-Part Coding Scheme for Networked Control under Fixed-Rate Constraints, 2022, 2022 IEEE International Symposium on Information Theory (ISIT)

Their frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Information Theory
  • Entropy
  • 2022 IEEE International Symposium on Information Theory (ISIT)
  • IEEE Transactions on Automatic Control

Collaboration has been a significant aspect of their research career, with frequent co-authors including:

  • Serdar Yüksel
  • Fady Alajaji
  • Bahman Gharesifard
  • László Györfi
  • Harro Walk

In 2013, Tamas Linder was awarded the distinction of IEEE Fellow for contributions to source coding and quantization.

Best Publications

  • Learning and design of principal curves

    B. Kegl;A. Krzyzak;T. Linder;K. Zeger

  • Distributed Online Convex Optimization on Time-Varying Directed Graphs

    Mohammad Akbari;Bahman Gharesifard;Tamas Linder

  • Rates of convergence in the source coding theorem, in empirical quantizer design, and in universal lossy source coding

    T. Linder;G. Lugosi;K. Zeger

  • The On-Line Shortest Path Problem Under Partial Monitoring

    András György;Tamás Linder;Gábor Lugosi;György Ottucsák

  • On the asymptotic tightness of the Shannon lower bound

    T. Linder;R. Zamir

  • Nonparametric estimation and classification using radial basis function nets and empirical risk minimization

    A. Krzyzak;T. Linder;C. Lugosi

  • The minimax distortion redundancy in empirical quantizer design

    P.L. Bartlett;T. Linder;G. Lugosi

  • Rényi divergence measures for commonly used univariate continuous distributions

    Manuel Gil;Fady Alajaji;Tamás Linder

  • Fast nearest-neighbor search in dissimilarity spaces

    A. Farago;T. Linder;G. Lugosi

  • Radial basis function networks and complexity regularization in function learning

    A. Krzyzak;T. Linder

  • Estimation Efficiency Under Privacy Constraints

    Shahab Asoodeh;Mario Diaz;Fady Alajaji;Tamas Linder

  • A zero-delay sequential scheme for lossy coding of individual sequences

    T. Linder;G. Lagosi

  • LEARNING-THEORETIC METHODS IN VECTOR QUANTIZATION

    T. Linder

  • Optimization and Convergence of Observation Channels in Stochastic Control

    Serdar Yüksel;Tamás Linder

  • High-resolution source coding for non-difference distortion measures: multidimensional companding

    T. Linder;R. Zamir;K. Zeger

  • Optimal entropy-constrained scalar quantization of a uniform source

    A. Gyorgy;T. Linder

  • Information Extraction Under Privacy Constraints

    Shahab Asoodeh;Mario Diaz;Fady Alajaji;Tamás Linder

  • A Polygonal Line Algorithm for Constructing Principal Curves

    Balázs Kégl;Adam Krzyzak;Tamás Linder;Kenneth Zeger

  • On the structure of optimal entropy-constrained scalar quantizers

    A. Gyorgy;T. Linder

  • Efficient Tracking of Large Classes of Experts

    A. Gyorgy;T. Linder;G. Lugosi

Frequent Co-Authors

Fady Alajaji
Fady Alajaji Queen's University
Gábor Lugosi
Gábor Lugosi Pompeu Fabra University
András György
András György New York University Abu Dhabi
Kenneth Zeger
Kenneth Zeger University of California, San Diego
Robert M. Gray
Robert M. Gray Stanford University
Ram Zamir
Ram Zamir Tel Aviv University
Adam Krzyżak
Adam Krzyżak Concordia University
Saeed Gazor
Saeed Gazor Queen's University
Michael W. Marcellin
Michael W. Marcellin University of Arizona
Sinan Gezici
Sinan Gezici Bilkent University

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