15.10.2020
Invited Presentation at 1st CIKM 2020 Workshop on Combining Symbolic and Sub-Symbolic Methods and Their Applications (CSSA-CIKM 2020)
Learning With Temporal Knowledge Graphs
MCML PI Volker Tresp, Yunpu Ma and Zhen Han review recently developed learning-based algorithms for temporal knowledge graphs completion and forecasting. Knowledge graphs, also known as episodic or time-dependent knowledge graphs are large-scale event databases that describe temporally evolving multi-relational data.
Related
09.10.2025
Rethinking AI in Public Institutions - Balancing Prediction and Capacity
Unai Fischer Abaigar explores how AI can make public decisions fairer, smarter, and more effective.
08.10.2025
MCML-LAMARR Workshop at University of Bonn
MCML and Lamarr researchers met in Bonn to exchange ideas on NLP, LLM finetuning, and AI ethics.
08.10.2025
Three MCML Members Win Best Paper Award at AutoML 2025
MCML PI Matthias Feurer and Director Bernd Bischl’s paper on overtuning won Best Paper at AutoML 2025, offering insights for robust HPO.
29.09.2025
Machine Learning for Climate Action - With Researcher Kerstin Forster
Kerstin Forster researches how AI can cut emissions, boost renewable energy, and drive corporate sustainability.
