04.11.2019
MCML at ACM SIGSPATIAL 2019
One Accepted Paper
27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Chicago, ILL, USA, Nov 05-08, 2019
We are happy to announce that MCML researchers have contributed a total of 1 paper to ACM SIGSPATIAL 2019. Congrats to our researchers!
Main Track (1 paper)
F. Borutta • S. Schmoll • S. Friedl
Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning.
ACM SIGSPATIAL 2019 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Chicago, ILL, USA, Nov 05-08, 2019. DOI
Optimizing the Spatio-Temporal Resource Search Problem with Reinforcement Learning.
ACM SIGSPATIAL 2019 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Chicago, ILL, USA, Nov 05-08, 2019. 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.