2 citations · 3 across the 12 of their papers we have counts for
12 papers
Consideration Set Sampling to Analyze Undecided Respondents
Dominik Kreiss, Thomas Augustin
Researchers in psychology characterize decision-making as a process of eliminating options. While statistical modelling typically focuses on the eventual choice, we analyze conside…
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…
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…
Approximately Bayes-Optimal Pseudo Label Selection
Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti +2
Semi-supervised learning by self-training heavily relies on pseudo-label selection (PLS). The selection often depends on the initial model fit on labeled data. Early overfitting mi…
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…
Accounting for Gaussian Process Imprecision in Bayesian Optimization
Julian Rodemann, Thomas Augustin
Bayesian optimization (BO) with Gaussian processes (GP) as surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we p…
How to Guide Decisions with Bayes Factors
Patrick Schwaferts, Thomas Augustin
Some scientific research questions ask to guide decisions and others do not. By their nature frequentist hypothesis-tests yield a dichotomous test decision as result, rendering the…
Bayes Factors can only Quantify Evidence w.r.t. Sets of Parameters, not w.r.t. (Prior) Distributions on the Parameter
Patrick Schwaferts, Thomas Augustin
Bayes factors are characterized by both the powerful mathematical framework of Bayesian statistics and the useful interpretation as evidence quantification. Former requires a param…
Practical Relevance: A Formal Definition
Patrick Schwaferts, Thomas Augustin
There is a general agreement that it is important to consider the practical relevance of an effect in addition to its statistical significance, yet a formal definition of practical…
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…
Towards a Paradigmatic Shift in Pre-election Polling Adequately Including Still Undecided Voters -- Some Ideas Based on Set-Valued Data for the 2021 German Federal Election
Dominik Kreiss, Thomas Augustin
Within this paper we develop and apply new methodology adequately including undecided voters for the 2021 German federal election. Due to a cooperation with the polling institute C…