Machine Learning Research Unit
Welcome to the web-home of the new Machine Learning research unit of TU Wien Informatics.
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Machine Learning Research Unit (E 194-06) Information Systems Engineering Institute Faculty of Informatics TU Wien Web: https://ml-tuw.github.io/ Physical: Gußhausstraße 27-29 (CA03), 1040 Vienna, Austria |
News
- Our paper on Kernel Methods for Predicting Yields of Chemical Reactions has been accepted for publication in the Journal of Chemical Information and Modeling. Joint work of Alexe Haywood, Joseph Redshaw, Magnus Hanson-Heine, Adam Taylor, Alex Brown, Andrew Mason, Thomas Gärtner , and Jonathan Hirst. (Oct '21)
- Our paper on Conditional Network Data Balancing With GANs has been accepted at the NeurIPS Deep Generative Models and Downstream Applications Workshop. Joint work ok Fares Meghdouri, Thomas Schmied, Tanja Zseby, and Thomas Gärtner . (Oct'21)
- Our paper on Active Learning Convex Halfspaces on Graphs (supplementary material) has been accepted at NeurIPS 2021. Congratulations to
Maximilian Thiessen
for his first NeurIPS paper! (Sept '21)
- Our team won the prize for the Best Practical Result in the Siemens AI-Dependability Assessment Challenge. Congratulations to
Joseph Redshaw
(University of Nottingham),
Maximilian Thiessen
(TU Wien), and
David Penz
(TU Wien)! (July '21)
- Our very own
David Penz
won the Distinguished Young Alumn award of TU Wien Informatics. Congratulations! (June '21)
- FFG has aggreed to fund our proposal on Artificial Intelligence for Advanced SAR Processing! (May '21)
- TU Wien has agreed to fund our doctoral college on Secure and Intelligent Human-Centric Digital Technologies ! (July '20)
- Founding of the Machine Learning research unit. (Feb '20)
- BBSRC has aggreed to fund our proposal on Bacteriphage to control Salmonella in pigs! (October '19)
Topics
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
- adversarial 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
- ...