World's Best Scientists 2026 revealed!
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Computer Science
Germany
2025

D-Index & Metrics

Computer Science

D-Index
79
Citations
51473
World Ranking
1113
National Ranking
43

Research.com Recognitions

  • 2025 - Research.com Computer Science in Germany Leader Award
  • 2023 - Research.com Computer Science in Germany Leader Award
  • 2022 - Research.com Computer Science in Germany Leader Award

Overview

Frank Hutter is affiliated with the University of Freiburg in Germany and specializes in computer science, with a strong focus on artificial intelligence. Their research incorporates multiple subfields, including computer vision and pattern recognition, molecular biology, computational theory and mathematics, and management science and operations research.

The scientist's main research topics include machine learning and data classification, advanced multi-objective optimization algorithms, advanced neural network applications, machine learning and algorithms, metaheuristic optimization algorithms research, evolutionary algorithms and applications, and adversarial robustness in machine learning.

Frequent publication venues for their work feature predominantly arXiv (Cornell University), alongside bioRxiv (Cold Spring Harbor Laboratory), the Journal of Artificial Intelligence Research, Zenodo (CERN European Organization for Nuclear Research), and the IEEE Transactions on Pattern Analysis and Machine Intelligence.

Among recent publications, notable papers include:

  • AI for social good: unlocking the opportunity for positive impact (2020, Nature Communications)
  • Accurate predictions on small data with a tabular foundation model (2025, Nature)
  • Machine-learning-based diagnostics of EEG pathology (2020, NeuroImage)
  • Managing extreme AI risks amid rapid progress (2024, Science)
  • Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL (2021, IEEE Transactions on Pattern Analysis and Machine Intelligence)

Hutter has collaborated extensively with several researchers, including Marius Lindauer, André Biedenkapp, Arber Zela, Jörg K. H. Franke, and Katharina Eggensperger, reflecting ongoing scientific partnerships.

In addition to journal articles, Hutter has contributed to book publications with Springer Science+Business Media, with titles such as Machine Learning and Knowledge Discovery in Databases published in 2021.

Best Publications

  • Decoupled Weight Decay Regularization.

    Ilya Loshchilov;Frank Hutter

  • SGDR: Stochastic Gradient Descent with Warm Restarts

    Ilya Loshchilov;Frank Hutter

  • Deep learning with convolutional neural networks for EEG decoding and visualization.

    Robin Tibor Schirrmeister;Jost Tobias Springenberg;Lukas Dominique Josef Fiederer;Martin Glasstetter

  • Sequential model-based optimization for general algorithm configuration

    Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

  • Neural Architecture Search: A Survey

    Thomas Elsken;Jan Hendrik Metzen;Frank Hutter

  • Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms

    Chris Thornton;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

  • Auto-sklearn: Efficient and Robust Automated Machine Learning

    Matthias Feurer;Aaron Klein;Katharina Eggensperger;Jost Tobias Springenberg

  • Efficient and robust automated machine learning

    Matthias Feurer;Aaron Klein;Katharina Eggensperger;Jost Tobias Springenberg

  • Fixing Weight Decay Regularization in Adam

    Ilya Loshchilov;Frank Hutter

  • SATzilla: portfolio-based algorithm selection for SAT

    Lin Xu;Frank Hutter;Holger H. Hoos;Kevin Leyton-Brown

  • Hyperparameter Optimization

    Unknown

  • ParamILS: An Automatic Algorithm Configuration Framework

    Frank Hutter;Thomas Stuetzle;Kevin Leyton-Brown;Holger H. Hoos

  • Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA

    Lars Kotthoff;Chris Thornton;Holger H. Hoos;Frank Hutter

  • Neural Architecture Search

    Thomas Elsken;Jan Hendrik Metzen;Frank Hutter

  • BOHB: Robust and Efficient Hyperparameter Optimization at Scale.

    Stefan Falkner;Aaron Klein;Frank Hutter

  • Algorithm runtime prediction: methods & evaluation

    Frank Hutter;Lin Xu;Holger H. Hoos;Kevin Leyton-Brown

  • Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves

    Tobias Domhan;Jost Tobias Springenberg;Frank Hutter

  • Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

    Aaron Klein;Stefan Falkner;Simon Bartels;Philipp Hennig

  • A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

    Patryk Chrabaszcz;Ilya Loshchilov;Frank Hutter

  • Performance prediction and automated tuning of randomized and parametric algorithms

    Frank Hutter;Youssef Hamadi;Holger H. Hoos;Kevin Leyton-Brown

  • Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution.

    Thomas Elsken;Jan Hendrik Metzen;Frank Hutter

Frequent Co-Authors

Holger H. Hoos
Holger H. Hoos RWTH Aachen University
Kevin Leyton-Brown
Kevin Leyton-Brown University of British Columbia
Jost Tobias Springenberg
Jost Tobias Springenberg University of Freiburg
Joaquin Vanschoren
Joaquin Vanschoren Eindhoven University of Technology
Tonio Ball
Tonio Ball University of Freiburg
Thomas Brox
Thomas Brox University of Freiburg
Thomas Stützle
Thomas Stützle Université Libre de Bruxelles
Bernd Bischl
Bernd Bischl Ludwig-Maximilians-Universität München
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Torsten Schaub
Torsten Schaub University of Potsdam

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