Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning.
Research questions concern for instance:
- learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space
- incorporation of invariances as well as expert domain knowledge in learning algorithms
- computational, sample, query, and communication complexity of learning algorithms
- constructive machine learning scenarios such as structured output prediction
- learning with small labelled data sets and large unlabelled data sets
- adverserial learning with mistake and/or regret bounds
- parallelisation/distribution of learning algorithms
- approximation of learning algorithms
- scalability of learning algorithms
- reliability of learning algorithms
- extreme learning
To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.
The research Unit Machine Learning is part of the Institute of Information Systems Engineering.
- Bachelor Thesis in Computer Science / 194.112 / PR
- Machine Learning / 184.702 / VU
- Machine Learning Algorithms and Applications / 194.101 / PR
- Project in Computer Science 1 - Machine Learning Algorithms and Applications / 194.119 / PR
- Scientific Research and Writing / 193.052 / SE
- Seminar for Master Students in Data Science / 180.772 / SE
- Seminar for PhD Students / 194.110 / SE
- Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
- Machine Learning / 184.702 / VU
Modelling Complex Structured Real Biological and Chemical Data using MachineLearning
2022 – 2023 / Austrian Exchange Service (OeAD)
Artificial Intelligence for Advanced SAR Processing
2021 – 2023 / Austrian Research Promotion Agency (FFG)
Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once APIs for the new database have been released, everything will be up to date again.
- One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems / P. Indri, A. Bartoli, E. Medvet, L. Nenzi / in: "EuroGP 2022: Proceedings of the 25th European Conference on Genetic Programming", 20-22 , April; Springer-Verlag, Madrid, Spain, 2022, 32 - 47
- LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis / M. Schedl, S. Brandl, O. Lesota, E. Parada-Cabaleiro, D. Penz, N. Rekabsaz / in: "CHIIR '22: ACM SIGIR Conference on Human Information Interaction and Retrieval", ACM, 2022, 337 - 341
- Kernel Methods for Predicting Yields of Chemical Reactions / A. Haywood, J. Redshaw, M. Hanson-Heine, A. Taylor, A. Brown, A. Mason, T. Gärtner, J. Hirst / Journal of Chemical Information and Modeling, 62 (2022), 9; 2077 pages
- Active Learning of Convex Halfspaces on Graphs / M. Thiessen, T. Gärtner / in: "Advances in Neural Information Processing Systems 34", Advances in Neural Information Processing Systems 34, 2021
- Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features / A. Krauck, D. Penz, M. Schedl / in: "RecSysChallenge 2021: RecSysChallenge '21: Proceedings of the Recommender Systems Challenge 2021", ACM, 2021, 39 - 43
- Machine Learning for Chemical Synthesis / T. Gärtner, A. Haywood, J. Redshaw, A. Taylor, A. Mason, J. Hirst / The Royal Society of Chemistry, London, 2020, ISBN: 978-1-78801-789-3; 25 pages
Soon, this page will include additional information such as reference projects, conferences, events, and other research activities.
Until then, please visit Machine Learning’s research profile in TISS .