2026 Matthias Seeger: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	42	8155	7925	398	386	104	15511

Overview

Matthias Seeger is affiliated with Amazon in Germany and has contributed significantly to the field of computer science through numerous research publications. The primary focus of their work lies at the intersection of artificial intelligence, machine learning, and operations research, with particular emphasis on Bayesian inference and optimization techniques.

The main fields of study covered by Matthias Seeger include:

Computer Science

Within this broad area, their subfields of research extend to:

Artificial Intelligence
Management Science and Operations Research
Computational Theory and Mathematics
Signal Processing
Computer Vision and Pattern Recognition

Seeger's research encompasses a range of topics, notably:

Machine Learning and Data Classification
Gaussian Processes and Bayesian Inference
Machine Learning and Algorithms
Advanced Multi-Objective Optimization Algorithms
Advanced Bandit Algorithms Research
Time Series Analysis and Forecasting
Forecasting Techniques and Applications

Their publication record features several papers predominantly appearing in the venue arXiv (Cornell University), totaling 14 publications, as well as one paper in Foundations and Trends® in Machine Learning.

Recent papers authored or coauthored by Matthias Seeger include:

"LEEP: A New Measure to Evaluate Transferability of Learned Representations" (2020) in arXiv (Cornell University)
"Cost-aware Bayesian Optimization" (2020) in arXiv (Cornell University)
"Model-based Asynchronous Hyperparameter and Neural Architecture Search" (2020) in arXiv (Cornell University)
"Overfitting in Bayesian Optimization: an empirical study and early-stopping solution" (2021) in arXiv (Cornell University)
"Amazon SageMaker Autopilot: a white box AutoML solution at scale" (2020) in arXiv (Cornell University)

Frequent collaborators working with Matthias Seeger include:

Cédric Archambeau
Valerio Perrone
Aaron Klein
Michele Donini
Luca Franceschi

Best Publications

Using the Nyström Method to Speed Up Kernel Machines

Christopher K. I. Williams;Matthias Seeger

2576
Citations
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Niranjan Srinivas;Andreas Krause;Matthias Seeger;Sham M. Kakade

1668
Citations
Gaussian processes for machine learning.

Matthias W. Seeger

1498
Citations
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

N. Srinivas;A. Krause;S. M. Kakade;M. Seeger

987
Citations
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

Niranjan Srinivas;Andreas Krause;Sham M. Kakade;Matthias Seeger

792
Citations
Learning with Labeled and Unlabeled Data

Matthias Seeger

736
Citations
Fast Sparse Gaussian Process Methods: The Informative Vector Machine

Ralf Herbrich;Neil D. Lawrence;Matthias Seeger

700
Citations
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

Matthias W. Seeger;Christopher K. I. Williams;Neil D. Lawrence

562
Citations
Deep State Space Models for Time Series Forecasting

Syama Sundar Rangapuram;Matthias W. Seeger;Jan Gasthaus;Lorenzo Stella

477
Citations
Model Learning with Local Gaussian Process Regression

Duy Nguyen-Tuong;Matthias W. Seeger;Jan Peters

405
Citations
Pac-bayesian generalisation error bounds for gaussian process classification

Matthias Seeger

384
Citations
Semiparametric Latent Factor Models

Yee Whye Teh;Matthias W. Seeger;Michael I. Jordan

294
Citations
Bayesian Inference and Optimal Design for the Sparse Linear Model

Matthias W. Seeger

292
Citations
Local Gaussian process regression for real time online model learning and control

Duy Nguyen-Tuong;Jan Peters;Matthias Seeger

262
Citations
The Effect of the Input Density Distribution on Kernel-based Classifiers

Christopher K. I. Williams;Matthias Seeger

233
Citations
Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations

Matthias Seeger

227
Citations
Expectation Propagation for Exponential Families

Matthias Seeger

204
Citations
Optimization of k-space trajectories for compressed sensing by Bayesian experimental design

Matthias Seeger;Hannes Nickisch;Rolf Pohmann;Bernhard Schölkopf

185
Citations
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

Matthias Seeger

177
Citations
Learning Inverse Dynamics: A Comparison

Duy Nguyen-Tuong;Jan Peters;Matthias W. Seeger;Bernhard Schölkopf

160
Citations
Local Gaussian Process Regression for Real Time Online Model Learning

Duy Nguyen-tuong;Jan R. Peters;Matthias Seeger

132
Citations
LEEP: A New Measure to Evaluate Transferability of Learned Representations

Cuong V. Nguyen;Tal Hassner;Matthias Seeger;Cedric Archambeau

47
Citations

Frequent Co-Authors

Jan Peters Technical University of Darmstadt

Bernhard Schölkopf Max Planck Institute for Intelligent Systems

Neil D. Lawrence University of Cambridge

Matthias Bethge University of Tübingen

Sham M. Kakade Harvard University

Andreas Krause ETH Zurich

Michael I. Jordan University of California, Berkeley

Ralf Herbrich Hasso Plattner Institute

Tal Hassner Facebook (United States)

Koji Tsuda University of Tokyo

External Links

Personal website of Matthias Seeger Google Scholar page

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