2 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Comparing Machine Learning Algorithms by Union-Free Generic Depth
Hannah Blocher, Georg Schollmeyer, Malte Nalenz +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is v…
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement
Christoph Jansen, Georg Schollmeyer, Hannah Blocher +2
Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Neverth…
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
Hannah Blocher, Georg Schollmeyer, Christoph Jansen +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric…
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning
Julian Rodemann, Christoph Jansen, Georg Schollmeyer +1
Self-training is a simple yet effective method within semi-supervised learning. The idea is to iteratively enhance training data by adding pseudo-labeled data. Its generalization p…