2 citations · 2 across the 10 of their papers we have counts for
10 papers
Robust Bayes Acts under Prior Perturbations: Contamination, Stability, and Selection Paths
Christoph Jansen, Georg Schollmeyer
This paper develops a quantitative framework to assess the robustness of Bayes-optimal decisions in finite decision problems under model uncertainty. We introduce two complementary…
Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation
Polina Gordienko, Christoph Jansen, Thomas Augustin +1
We propose a framework for probability aggregation based on propositional probability logic. Unlike conventional judgment aggregation, which focuses on static rationality, our mode…
Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information
Christoph Jansen
This habilitation thesis is cumulative and, therefore, is collecting and connecting research that I (together with several co-authors) have conducted over the last few years. Thus,…
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…
Multi-Target Decision Making under Conditions of Severe Uncertainty
Christoph Jansen, Georg Schollmeyer, Thomas Augustin
The quality of consequences in a decision making problem under (severe) uncertainty must often be compared among different targets (goals, objectives) simultaneously. In addition,…
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Christoph Jansen, Malte Nalenz, Georg Schollmeyer +1
Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect…
Information efficient learning of complexly structured preferences: Elicitation procedures and their application to decision making under uncertainty
Christoph Jansen, Hannah Blocher, Thomas Augustin +1
In this paper we propose efficient methods for elicitation of complexly structured preferences and utilize these in problems of decision making under (severe) uncertainty. Based on…