4 citations · 5 across the 3 of their papers we have counts for
6 papers
Real-World Networks are Low-Dimensional: Theoretical and Practical Assessment
Tobias Friedrich, Andreas Göbel, Maximilian Katzmann +1
Detecting the dimensionality of graphs is a central topic in machine learning. While the problem has been tackled empirically as well as theoretically, existing methods have severa…
Cliques in High-Dimensional Geometric Inhomogeneous Random Graphs
Tobias Friedrich, Andreas Göbel, Maximilian Katzmann +1
A recent trend in the context of graph theory is to bring theoretical analyses closer to empirical observations, by focusing the studies on random graph models that are used to rep…
Computing Voronoi Diagrams in the Polar-Coordinate Model of the Hyperbolic Plane
Tobias Friedrich, Maximilian Katzmann, Leon Schiller
A Voronoi diagram is a basic geometric structure that partitions the space into regions associated with a given set of sites, such that all points in a region are closer to the cor…
Learning Languages with Decidable Hypotheses
Julian Berger, Maximilian Böther, Vanja Doskoč +9
In language learning in the limit, the most common type of hypothesis is to give an enumerator for a language. This so-called -index allows for naming arbitrary computably enume…
Maps for Learning Indexable Classes
Julian Berger, Maximilian Böther, Vanja Doskoč +9
We study learning of indexed families from positive data where a learner can freely choose a hypothesis space (with uniformly decidable membership) comprising at least the language…
A Strategic Routing Framework and Algorithms for Computing Alternative Paths
Thomas Bläsius, Maximilian Böther, Philipp Fischbeck +9
Traditional navigation services find the fastest route for a single driver. Though always using the fastest route seems desirable for every individual, selfish behavior can have un…