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, Rainer Goebel, Alexander Kröner, 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

Peers

Kurt Driessens
Comparison fields: 5 of 96
  • Artificial Intelligence 601
  • Computer Vision and Pattern Recognition 193
  • Computational Theory and Mathematics 84
  • Control and Systems Engineering 63
  • Information Systems 60
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Citations per field, relative to Kurt Driessens
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Citations per year, relative to Kurt Driessens
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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
# Work Indexed citations
1 1
2 4
3 0
4 1
5 4
6
Largest Source Subset Selection for Instance Transfer
4
7
An automated measure of MDP similarity for transfer in reinforcement learning
29
8 32
9
Automatic discretization of actions and states in Monte-Carlo tree search
6
10 1
11
Prediction of Congested Traffic on the Critical Density Point Using Machine Learning and Decentralised Collaborating Cameras
4
12
Learning a transfer function for reinforcement learning problems
4
13
Bayes-relational learning of opponent models from incomplete information in no-limit poker
15
14
Transfer learning for reinforcement learning through goal and policy parametrization
8
15
Combining Model-Based and Instance-Based Learning for First Order Regression.
3
16
Thesis: relational reinforcement learning
2
17
On the numeric stability of Gaussian processes regression for relational reinforcement learning
14
18
Relational instance based regression for relational reinforcement learning
34
19
Integrating Experimentation and Guidance in Relational Reinforcement Learning
19
20
First order models for the predictive toxicology challenge
2

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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