World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
10837
World Ranking
8649
National Ranking
426

Overview

Manfred Opper is affiliated with the Technical University of Berlin in Germany. Their research primarily focuses on Computer Science, with particular attention to Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, Control and Systems Engineering, and Electrical and Electronic Engineering.

They have published extensively in several areas, including Gaussian Processes and Bayesian Inference, Model Reduction and Neural Networks, stochastic dynamics and bifurcation, Gene Regulatory Network Analysis, Fault Detection and Control Systems, earthquake and tectonic studies, and Advanced MIMO Systems Optimization.

Their recent publications demonstrate a range of topics and methodologies in the field. Notable papers include:

  • Interacting Particle Solutions of Fokker-Planck Equations Through Gradient-Log-Density Estimation, 2020, Entropy
  • GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model, 2022, Statistics and Computing
  • A mathematical model of local and global attention in natural scene viewing, 2020, PLoS Computational Biology
  • Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation, 2021, Entropy
  • Joint Message Detection and Channel Estimation for Unsourced Random Access in Cell-Free User-Centric Wireless Networks, 2025, IEEE Transactions on Information Theory

Manfred Opper frequently collaborates with several researchers, including:

  • Burak Çakmak
  • Sebastian Reich
  • César Ojeda
  • Dimitra Maoutsa
  • Noa Malem-Shinitski

Their publication record spans a number of prominent venues, with the highest concentration in arXiv (Cornell University), where they have contributed 18 papers. Other venues include Entropy, Statistics and Computing, PLoS Computational Biology, and IEEE Transactions on Information Theory.

Best Publications

  • Query by committee

    H. S. Seung;M. Opper;H. Sompolinsky

  • Sparse on-line Gaussian processes

    Lehel Csató;Manfred Opper

  • Advanced mean field methods: theory and practice

    Manfred Opper;David Saad

  • The variational gaussian approximation revisited

    Manfred Opper;Cédric Archambeau

  • Optimal control as a graphical model inference problem

    Hilbert J. Kappen;Vicenç Gómez;Manfred Opper

  • Gaussian Processes for Classification: Mean-Field Algorithms

    Manfred Opper;Ole Winther

  • Learning of correlated patterns in spin-glass networks by local learning rules.

    Sigurd Diederich;Manfred Opper

  • Expectation Consistent Approximate Inference

    Manfred Opper;Ole Winther

  • A Bayesian approach to on-line learning

    Manfred Opper

  • Generalization performance of Bayes optimal classification algorithm for learning a perceptron.

    Manfred Opper;David Haussler

  • Optimal control as a graphical model inference problem

    B. Kappen;V. Gomez;M. Opper

  • Sparse Representation for Gaussian Process Models

    Lehel Csató;Manfred Opper

  • MUTUAL INFORMATION, METRIC ENTROPY AND CUMULATIVE RELATIVE ENTROPY RISK

    David Haussler;Manfred Opper

  • On the ability of the optimal perceptron to generalise

    M Opper;W Kinzel;J Kleinz;R Nehl

  • Statistical mechanics of Support Vector networks.

    Rainer Dietrich;Manfred Opper;Haim Sompolinsky

  • Statistical mechanics of generalization

    Manfred Opper

  • Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling.

    Manfred Opper;Ole Winther

  • Advances in Neural Information Processing Systems 26 (NIPS 2013)

    Botond Cseke;Manfred Opper;Guido Sanguinetti

  • Common Input Explains Higher-Order Correlations and Entropy in a Simple Model of Neural Population Activity

    Jakob H. Macke;Manfred Opper;Matthias Bethge

  • Comparing the Mean Field Method and Belief Propagation for Approximate Inference in MRFs

    Manfred Opper;David Saad

  • Advances in Neural Information Processing Systems 11 (NIPS 1998)

    Giancarlo Ferrari Trecate;Christopher Williams;Manfred Opper

  • Tutorial on Variational Approximation Methods

    Manfred Opper;David Saad

Frequent Co-Authors

Ole Winther
Ole Winther Technical University of Denmark
Guido Sanguinetti
Guido Sanguinetti International School for Advanced Studies
David Saad
David Saad Aston University
Ron Meir
Ron Meir Technion – Israel Institute of Technology
Jakob H. Macke
Jakob H. Macke Max Planck Institute for Intelligent Systems
Matthias Bethge
Matthias Bethge University of Tübingen
David Haussler
David Haussler University of California, Santa Cruz
Bernard Henri Fleury
Bernard Henri Fleury Aalborg University
John Shawe-Taylor
John Shawe-Taylor University College London
Haim Sompolinsky
Haim Sompolinsky Hebrew University of Jerusalem

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