20.04.2020
MCML at WWW 2020
Two Accepted Papers (2 Workshops)
International World Wide Web Conference, Taipeh, Taiwan, Apr 21, 2020
We are happy to announce that MCML researchers have contributed a total of 2 papers to WWW 2020: 2 Workshop papers. Congrats to our researchers!
Workshops (2 papers)
M. Berrendorf • E. Faerman • V. Tresp
Active Learning for Entity Alignment.
DL4G @WWW 2020 - 5th International Workshop on Deep Learning for Graphs at the International World Wide Web Conference. Taipeh, Taiwan, Apr 21, 2020. arXiv
Active Learning for Entity Alignment.
DL4G @WWW 2020 - 5th International Workshop on Deep Learning for Graphs at the International World Wide Web Conference. Taipeh, Taiwan, Apr 21, 2020. arXiv
M. Berrendorf • E. Faerman • L. Vermue • V. Tresp
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank (Extended Abstract).
DL4G @WWW 2020 - 5th International Workshop on Deep Learning for Graphs at the International World Wide Web Conference. Taipeh, Taiwan, Apr 21, 2020. Full paper at WI-AT 2020. DOI
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank (Extended Abstract).
DL4G @WWW 2020 - 5th International Workshop on Deep Learning for Graphs at the International World Wide Web Conference. Taipeh, Taiwan, Apr 21, 2020. Full paper at WI-AT 2020. DOI
Related
26.08.2026
Transparency for Global Health Aid
MCML PI Stefan Feuerriegel and his team use machine learning to uncover disparities in the global allocation of health aid.
25.08.2026
David Rügamer: We Need Uncertainty Quantification
MCML PI David Rügamer explains why uncertainty quantification is essential for building trustworthy AI and making informed decisions.
