24.05.2023
MCML at PAKDD 2023
Two Accepted Papers
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Osaka, Japan, May 25-28, 2023
We are happy to announce that MCML researchers have contributed a total of 2 papers to PAKDD 2023. Congrats to our researchers!
Main Track (2 papers)
T. Weber • M. Ingrisch • B. Bischl • D. Rügamer
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
PAKDD 2023 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Osaka, Japan, May 25-28, 2023. DOI
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
PAKDD 2023 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Osaka, Japan, May 25-28, 2023. DOI
D. Winkel • N. Strauß • M. Schubert • Y. Ma • T. Seidl
Constrained Portfolio Management using Action Space Decomposition for Reinforcement Learning.
PAKDD 2023 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Osaka, Japan, May 25-28, 2023. DOI
Constrained Portfolio Management using Action Space Decomposition for Reinforcement Learning.
PAKDD 2023 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Osaka, Japan, May 25-28, 2023. 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.
