Frank Hutter
Impact in
- Artificial Intelligence top 0.05%
- Machine Learning and Data Classification
- Metaheuristic Optimization Algorithms Research
- Machine Learning and Algorithms
- Evolutionary Algorithms and Applications
- Computational Theory and Mathematics top 0.1%
- Advanced Multi-Objective Optimization Algorithms
Papers in
-
- Machine Learning and Data Classification 66
- Machine Learning and Algorithms 47
- Metaheuristic Optimization Algorithms Research 20
- Evolutionary Algorithms and Applications 9
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- Advanced Multi-Objective Optimization Algorithms 36
- Formal Methods in Verification 10
- Co-authors
- Holger H. Hoos (49 shared papers)Kevin Leyton‐Brown (35 shared papers)Matthias Feurer (10 shared papers)Jost Tobias Springenberg (11 shared papers)Katharina Eggensperger (13 shared papers)Lars Kotthoff (6 shared papers)Joaquin Vanschoren (5 shared papers)Ilya Loshchilov (4 shared papers)
- Journals
- Journal of Artificial Intelligence Research (9 papers)Lecture notes in computer science (27 papers)Artificial Intelligence (3 papers)International Journal of Computer Vision (1 paper)Bioinformatics (1 paper)
- Partner nations
- GermanyCanadaUnited States
In The Last Decade
Frank Hutter
139 papers receiving 16.5k citations
Frank Hutter's Hit Papers
Peers
Comparison fields: 5 of 207
- Artificial Intelligence 9.1k
- Computational Theory and Mathematics 2.7k
- Software 636
- Cognitive Neuroscience 2.5k
- Signal Processing 1.2k
Countries citing papers authored by Frank Hutter
This map shows the geographic impact of Frank Hutter's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Frank Hutter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frank Hutter more than expected).
Fields of papers citing papers by Frank Hutter
This network shows the impact of papers produced by Frank Hutter. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Frank Hutter. The network helps show where Frank Hutter may publish in the future.
Co-authors
The 25 scholars most cited alongside Frank Hutter, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 151 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep learning with convolutional neural networks for EEG decoding and visualization Hit paper breakdown → | 2017 | 2504 |
| 2 | Sequential Model-Based Optimization for General Algorithm Configuration Hit paper breakdown → | 2011 | 1613 |
| 3 | Automated Machine Learning Hit paper breakdown → | 2019 | 1062 |
| 4 | Auto-WEKA Hit paper breakdown → | 2013 | 996 |
| 5 | Efficient and robust automated machine learning Hit paper breakdown → | 2015 | 893 |
| 6 | Hyperparameter Optimization Hit paper breakdown → | 2019 | 885 |
| 7 | Fixing Weight Decay Regularization in Adam Hit paper breakdown → | 2018 | 770 |
| 8 | ParamILS: An Automatic Algorithm Configuration Framework Hit paper breakdown → | 2009 | 691 |
| 9 | SATzilla: Portfolio-based Algorithm Selection for SAT Hit paper breakdown → | 2008 | 623 |
| 10 | Neural Architecture Search Hit paper breakdown → | 2019 | 547 |
| 11 | Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA Hit paper breakdown → | 2019 | 424 |
| 12 | Algorithm runtime prediction: Methods & evaluation Hit paper breakdown → | 2013 | 301 |
| 13 | Accurate predictions on small data with a tabular foundation model Hit paper breakdown → | 2025 | 301 |
| 14 | 2019 | 278 | |
| 15 | Initializing Bayesian Hyperparameter Optimization via Meta-Learning Hit paper breakdown → | 2015 | 267 |
| 16 | 2016 | 250 | |
| 17 | Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves | 2015 | 247 |
| 18 | An Efficient Approach for Assessing Hyperparameter Importance | 2014 | 228 |
| 19 | Automatic algorithm configuration based on local search | 2007 | 202 |
| 20 | 2002 | 156 |
About Frank Hutter
Frank Hutter is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Software, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 151 papers that have together received 17.0k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (66 papers), Machine Learning and Algorithms (47 papers), Advanced Multi-Objective Optimization Algorithms (36 papers), Constraint Satisfaction and Optimization (22 papers), Metaheuristic Optimization Algorithms Research (20 papers), Advanced Neural Network Applications (12 papers), Formal Methods in Verification (10 papers) and Evolutionary Algorithms and Applications (9 papers). The work is most often cited by research in Artificial Intelligence (9.1k citations), Computational Theory and Mathematics (2.7k citations), Software (636 citations), Cognitive Neuroscience (2.5k citations) and Signal Processing (1.2k citations). Frank Hutter has collaborated with scholars based in Germany, Canada and United States. Frequent co-authors include Holger H. Hoos, Kevin Leyton‐Brown, Matthias Feurer, Jost Tobias Springenberg, Katharina Eggensperger, Lars Kotthoff, Joaquin Vanschoren, Ilya Loshchilov, Robin Tibor Schirrmeister and Chris Thornton. Their work appears in journals such as Journal of Artificial Intelligence Research, Lecture notes in computer science, Artificial Intelligence, International Journal of Computer Vision and Bioinformatics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.