Kurt Driessens

2.0k total citations
52 papers, 852 citations indexed

About

Kurt Driessens is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Kurt Driessens has authored 52 papers receiving a total of 852 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Control and Systems Engineering. Recurrent topics in Kurt Driessens's work include Reinforcement Learning in Robotics (13 papers), Evolutionary Algorithms and Applications (8 papers) and Domain Adaptation and Few-Shot Learning (7 papers). Kurt Driessens is often cited by papers focused on Reinforcement Learning in Robotics (13 papers), Evolutionary Algorithms and Applications (8 papers) and Domain Adaptation and Few-Shot Learning (7 papers). Kurt Driessens collaborates with scholars based in Netherlands, Belgium and Germany. Kurt Driessens's co-authors include Sašo Džeroski, Luc De Raedt, Jan Ramon, Alexander Kröner, Rainer Goebel, Mario Senden, Karl Tuyls, Haitham Bou Ammar, Gerhard Weiß and Kristian Kersting and has published in prestigious journals such as PLoS ONE, The Journal of Physical Chemistry C and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Kurt Driessens

47 papers receiving 760 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kurt Driessens 601 193 84 63 60 52 852
Vaishak Belle 769 1.3× 283 1.5× 88 1.0× 24 0.4× 112 1.9× 83 1.2k
Kuo-Kun Tseng 269 0.4× 243 1.3× 120 1.4× 34 0.5× 40 0.7× 49 737
Nandakishore Kambhatla 315 0.5× 224 1.2× 64 0.8× 52 0.8× 62 1.0× 12 703
Kamlesh Mistry 304 0.5× 309 1.6× 52 0.6× 37 0.6× 35 0.6× 27 733
Asim Roy 336 0.6× 123 0.6× 60 0.7× 79 1.3× 37 0.6× 49 608
Masahito Kurihara 269 0.4× 99 0.5× 61 0.7× 31 0.5× 96 1.6× 81 520
Michael Riley 2.0k 3.4× 197 1.0× 154 1.8× 30 0.5× 63 1.1× 103 2.3k
Martin Loomes 244 0.4× 105 0.5× 60 0.7× 22 0.3× 104 1.7× 56 582
Eun‐Soo Kim 180 0.3× 192 1.0× 53 0.6× 48 0.8× 117 1.9× 65 659
Nicolas Monmarché 244 0.4× 93 0.5× 95 1.1× 50 0.8× 38 0.6× 34 497

Countries citing papers authored by Kurt Driessens

Since Specialization
Citations

This map shows the geographic impact of Kurt Driessens'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 Kurt Driessens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kurt Driessens more than expected).

Fields of papers citing papers by Kurt Driessens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kurt Driessens. 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 Kurt Driessens. The network helps show where Kurt Driessens may publish in the future.

Co-authorship network of co-authors of Kurt Driessens

This figure shows the co-authorship network connecting the top 25 collaborators of Kurt Driessens. A scholar is included among the top collaborators of Kurt Driessens based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kurt Driessens. Kurt Driessens is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Tiantian, Joël Karel, Kurt Driessens, et al.. (2025). Deep learning based estimation of heart surface potentials. Artificial Intelligence in Medicine. 163. 103093–103093. 1 indexed citations
2.
Portilla, M. Lopez, A. L. Miller, S. Schmidt, et al.. (2024). Detection of anomalies amongst LIGO’s glitch populations with autoencoders. Classical and Quantum Gravity. 41(5). 55004–55004. 4 indexed citations
3.
Cellier, Peggy & Kurt Driessens. (2020). Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. Communications in computer and information science. 1167. 1 indexed citations
4.
Driessens, Kurt, Evgueni Smirnov, Michael Lenz, et al.. (2020). Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes. PLoS ONE. 15(8). e0236392–e0236392. 4 indexed citations
5.
Zhou, Shuang, et al.. (2015). Largest Source Subset Selection for Instance Transfer. Research Publications (Maastricht University). 423–438. 4 indexed citations
6.
Ammar, Haitham Bou, Eric Eaton, Matthew E. Taylor, et al.. (2014). An automated measure of MDP similarity for transfer in reinforcement learning. National Conference on Artificial Intelligence. 31–37. 29 indexed citations
7.
Ammar, Haitham Bou, Karl Tuyls, Matthew E. Taylor, Kurt Driessens, & Gerhard Weiß. (2012). Reinforcement learning transfer via sparse coding. Adaptive Agents and Multi-Agents Systems. 383–390. 32 indexed citations
8.
Broeck, Guy Van den & Kurt Driessens. (2011). Automatic discretization of actions and states in Monte-Carlo tree search. Lirias (KU Leuven). 1–12. 6 indexed citations
9.
Ponsen, Marc, et al.. (2009). Learning with whom to communicate using relational reinforcement learning. Research portal (Tilburg University). 1221–1222. 1 indexed citations
10.
Labeeuw, Wouter, Kurt Driessens, Danny Weyns, Tom Holvoet, & Geert Deconinck. (2009). Prediction of Congested Traffic on the Critical Density Point Using Machine Learning and Decentralised Collaborating Cameras. Lirias (KU Leuven). 15–26. 4 indexed citations
11.
Croonenborghs, Tom, Kurt Driessens, & Maurice Bruynooghe. (2008). Learning a transfer function for reinforcement learning problems. Lirias (KU Leuven). 15–16. 4 indexed citations
12.
Ponsen, Marc, Jan Ramon, Tom Croonenborghs, Kurt Driessens, & Karl Tuyls. (2008). Bayes-relational learning of opponent models from incomplete information in no-limit poker. TU/e Research Portal. 1485–1486. 15 indexed citations
13.
Driessens, Kurt, Jan Ramon, & Tom Croonenborghs. (2006). Transfer learning for reinforcement learning through goal and policy parametrization. Lirias (KU Leuven). 1–4. 8 indexed citations
14.
Driessens, Kurt & Sašo Džeroski. (2005). Combining Model-Based and Instance-Based Learning for First Order Regression.. 341–342. 3 indexed citations
15.
Driessens, Kurt. (2005). Thesis: relational reinforcement learning. AI Communications. 18(1). 71–73. 2 indexed citations
16.
Ramon, Jan & Kurt Driessens. (2004). On the numeric stability of Gaussian processes regression for relational reinforcement learning. Lirias (KU Leuven). 10–14. 14 indexed citations
17.
Driessens, Kurt & Jan Ramon. (2003). Relational instance based regression for relational reinforcement learning. Lirias (KU Leuven). 123–130. 34 indexed citations
18.
Driessens, Kurt & Sašo Džeroski. (2002). Integrating Experimentation and Guidance in Relational Reinforcement Learning. Lirias (KU Leuven). 115–122. 19 indexed citations
19.
Blockeel, Hendrik, Kurt Driessens, Jan Ramon, et al.. (2001). First order models for the predictive toxicology challenge. Lirias (KU Leuven). 1–12. 2 indexed citations
20.
Driessens, Kurt & Hendrik Blockeel. (2001). Learning digger using hierarchical reinforcement learning for concurrent goals. Lirias (KU Leuven). 11–12. 5 indexed citations

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.

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