9 citations · 10 across the 3 of their papers we have counts for
3 papers
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Maximilian Kertel, Stefan Harmeling, Markus Pauly
Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process c…
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
Lena Schmid, Alexander Gerharz, Andreas Groll +1
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of…
pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules
Michael Kirchhof, Lena Schmid, Christopher Reining +2
A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables applicatio…