3 papers
cs.LG2025
Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs
KutalmıŠCoÅkun, Ivo Kavisanczki, Amin Mirzaei +4
In complex and low-data domains such as biomedical research, incorporating background knowledge (BK) graphs, such as protein-protein interaction (PPI) networks, into graph-based ma…
cs.LG2025
SubROC: AUC-Based Discovery of Exceptional Subgroup Performance for Binary Classifiers
Tom Siegl, KutalmıŠCoÅkun, Bjarne C. Hiller +3
Machine learning (ML) is increasingly employed in real-world applications like medicine or economics, thus, potentially affecting large populations. However, ML models often do not…
cs.LG2024
Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams
KutalmıŠCoÅkun, Borahan Tümer, Bjarne C. Hiller +1
Markov chains are simple yet powerful mathematical structures to model temporally dependent processes. They generally assume stationary data, i.e., fixed transition probabilities b…