3 papers
cs.LG2024
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
Andrej Tschalzev, Sascha Marton, Stefan Lüdtke +2
Tabular data is prevalent in real-world machine learning applications, and new models for supervised learning of tabular data are frequently proposed. Comparative studies assessing…
cs.AI2024
A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
Patrick Betz, Nathanael Stelzner, Christian Meilicke +2
In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph…
cs.AI2024
Reevaluation of Inductive Link Prediction
Simon Ott, Christian Meilicke, Heiner Stuckenschmidt
Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of ran…