23 citations · 37 across the 6 of their papers we have counts for
8 papers
The Impact of Dimensionality on the Stability of Node Embeddings
Tobias Schumacher, Simon Reichelt, Markus Strohmaier
Previous work has shown that node embedding methods can produce different representations and downstream predictions across repeated training runs, even when trained on the same da…
What Do Temporal Graph Learning Models Learn?
Abigail J. Hayes, Tobias Schumacher, Markus Strohmaier
Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models. Howe…
ReSi: A Comprehensive Benchmark for Representational Similarity Measures
Max Klabunde, Tassilo Wald, Tobias Schumacher +3
Measuring the similarity of different representations of neural architectures is a fundamental task and an open research challenge for the machine learning community. This paper pr…
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Max Klabunde, Tobias Schumacher, Markus Strohmaier +1
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a compreh…
Properties of Group Fairness Metrics for Rankings
Tobias Schumacher, Marlene Lutz, Sandipan Sikdar +1
In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and rank…
A Comparative Evaluation of Quantification Methods
Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
Quantification represents the problem of estimating the distribution of class labels on unseen data. It also represents a growing research field in supervised machine learning, for…