Scholar
Thomas Gärtner
Google Scholar ID: sOI8QyoAAAAJ
TU Wien (Technical University of Vienna)
Citations & Impact
Citations
1,865
H-index
21
i10-index
34
Publications
20
Co-authors
29
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- Published a paper on Scalable Learning in Reproducing Kernel Kreı̆n Spaces at ICML'19 (A*, top 4%); Published a paper on Learning in Reproducing Kernel Kreı̆n Spaces at ICML'18 (A*, top 4%); A submission on Active Search for Computer-Aided Drug Design was accepted by the Molecular Informatics special issue; Published a paper on Effective Parallelisation for Machine Learning at NIPS'17 (A*, top 4%), with a preprint and teaser video available online; Published a paper on Co-regularised support vector regression at ECML PKDD'17 (A, top 18%), which received the Best Paper Award from the Data Mining in Biomedical Informatics and Healthcare workshop'16; Published a paper on Active Search in Intensionally Specified Structured Spaces at AAAI'17 (A*, top 4%); Published a paper on Nyström Method with Kernel K-means++ Samples as Landmarks at ICML'17 (A*, top 4%). All these works were done in collaboration with other researchers.
Research Experience
- Joined TU Wien in October 2019 as a Professor of Machine Learning; Since 2019, has been a member of the Steering Committee of ECML PKDD and the competence center for machine learning Rhine-Ruhr ML2R; Served as Program Co-chair for ECML PKDD 2018, the largest European conference on Machine Learning and Data Mining (A, top 18%).
Background
- Main research interests: efficient and effective machine learning and data mining algorithms; Areas of focus: constructive machine learning, structured output prediction, active learning/search, online learning/optimisation, knowledge-based learning, etc.
Miscellany
- We are hiring!!!
