Javier García
- Artificial Intelligence top 2%
- Control and Systems Engineering top 5%
- Automotive Engineering top 5%
- Computational Theory and Mathematics top 5%
- Electrical and Electronic Engineering
- Co-authors
- Fernando FernándezManuela VelosoÁngel García‐OlayaÁlvaro TorralbaDaniel BorrajoCarlos Linares LópezCarlos V. RegueiroRebeca Marfil
- Topics
- Reinforcement Learning in Robotics (14 papers)Adversarial Robustness in Machine Learning (6 papers)Evolutionary Algorithms and Applications (6 papers)
In The Last Decade
Javier García
24 papers receiving 939 citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Artificial Intelligence 535
- Control and Systems Engineering 356
- Automotive Engineering 146
- Computational Theory and Mathematics 134
- Electrical and Electronic Engineering 107
Countries citing papers authored by Javier García
This map shows the geographic impact of Javier García'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 Javier García with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Javier García more than expected).
Fields of papers citing papers by Javier García
This network shows the impact of papers produced by Javier García. 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 Javier García. The network helps show where Javier García may publish in the future.
Co-authorship network of co-authors of Javier García
This figure shows the co-authorship network connecting the top 25 collaborators of Javier García. A scholar is included among the top collaborators of Javier García 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 Javier García. Javier García is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 5 | |
| 7 | 5 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | 15 | |
| 11 | 33 | |
| 12 | 13 | |
| 13 | 49 | |
| 14 | 3 | |
| 15 | A comprehensive survey on safe reinforcement learningbreakdown → | 655 |
| 16 | 7 | |
| 17 | 4 | |
| 18 | 18 | |
| 19 | 58 | |
| 20 | Reinforcement learning in the robocup-soccer keepaway | 3 |
About Javier García
Javier García is a scholar working on Artificial Intelligence, Management Information Systems and Control and Systems Engineering, having authored 26 papers that have together received 972 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (14 papers), Adversarial Robustness in Machine Learning (6 papers) and Evolutionary Algorithms and Applications (6 papers). The work is most often cited by research in Artificial Intelligence (535 citations), Control and Systems Engineering (356 citations) and Automotive Engineering (146 citations). Javier García has collaborated with scholars based in Spain, France and Portugal. Frequent co-authors include Fernando Fernández, Manuela Veloso, Ángel García‐Olaya, Álvaro Torralba, Daniel Borrajo, Carlos Linares López, Carlos V. Regueiro, Rebeca Marfil, Antonio Bandera and Roberto Iglesias. Their work appears in journals such as European Journal of Operational Research, Expert Systems with Applications and Sensors.
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.