Wouter Kool
Impact in
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- Vehicle Routing Optimization Methods
- Advanced Manufacturing and Logistics Optimization
- Optimization and Packing Problems
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- Metaheuristic Optimization Algorithms Research
- Reinforcement Learning in Robotics
- Topic Modeling
Papers in
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- Neural Networks and Applications 2
- Machine Learning and Data Classification 2
- Machine Learning and Algorithms 2
- Bayesian Modeling and Causal Inference 1
- Adversarial Robustness in Machine Learning 1
- Reinforcement Learning in Robotics 1
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- Vehicle Routing Optimization Methods 3
- Co-authors
- Max Welling (5 shared papers)Herke van Hoof (5 shared papers)Iris A. M. Huijben (2 shared papers)Ruud J. G. van Sloun (2 shared papers)Zachariah M. Reagh (1 shared paper)
- Journals
- INFORMS journal on computing (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Journal of Machine Learning Research (1 paper)Journal of Experimental Psychology General (1 paper)International Conference on Learning Representations (1 paper)
- Partner nations
- NetherlandsUnited States
In The Last Decade
Wouter Kool
8 papers receiving 168 citations
Peers
Comparison fields: 5 of 55
- Industrial and Manufacturing Engineering 54
- Artificial Intelligence 79
- Automotive Engineering 20
- Computer Vision and Pattern Recognition 31
- Computer Networks and Communications 28
Countries citing papers authored by Wouter Kool
This map shows the geographic impact of Wouter Kool'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 Wouter Kool with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wouter Kool more than expected).
Fields of papers citing papers by Wouter Kool
This network shows the impact of papers produced by Wouter Kool. 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 Wouter Kool. The network helps show where Wouter Kool may publish in the future.
Co-authors
The 5 scholars most cited alongside Wouter Kool, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 78 | |
| 2 | 2022 | 39 | |
| 3 | 2024 | 13 | |
| 4 | Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement | 2020 | 12 |
| 5 | Attention Solves Your TSP, Approximately | 2018 | 11 |
| 6 | 2023 | 11 | |
| 7 | Buy 4 REINFORCE Samples, Get a Baseline for Free! | 2019 | 7 |
| 8 | 2020 | 4 | |
| 9 | 2021 | 0 |
About Wouter Kool
Wouter Kool is a scholar working on Artificial Intelligence, Industrial and Manufacturing Engineering, Automotive Engineering, Computational Theory and Mathematics and Transportation, having authored 9 papers that have together received 175 indexed citations. Recurring topics across this work include Vehicle Routing Optimization Methods (3 papers), Transportation and Mobility Innovations (2 papers), Neural Networks and Applications (2 papers), Machine Learning and Data Classification (2 papers), Machine Learning and Algorithms (2 papers), Bayesian Modeling and Causal Inference (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (54 citations), Artificial Intelligence (79 citations), Automotive Engineering (20 citations), Computer Vision and Pattern Recognition (31 citations) and Computer Networks and Communications (28 citations). Wouter Kool has collaborated with scholars based in Netherlands and United States. Frequent co-authors include Max Welling, Herke van Hoof, Iris A. M. Huijben, Ruud J. G. van Sloun and Zachariah M. Reagh. Their work appears in journals such as INFORMS journal on computing, IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Machine Learning Research, Journal of Experimental Psychology General and International Conference on Learning Representations.
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.