4 citations · 8 across the 26 of their papers we have counts for
9 papers · 1 filter
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
Julian Rodemann, Alexander Marquard, Thomas Augustin +1
Approximate Bayesian inference typically revolves around computing the posterior parameter distribution. In practice, however, the main object of interest is often a model's predic…
Performative Learning Theory
Julian Rodemann, Unai Fischer-Abaigar, James Bailie +1
Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only existing users of an app) and/or the w…
Reciprocal Learning
Julian Rodemann, Christoph Jansen, Georg Schollmeyer
We demonstrate that a wide array of machine learning algorithms are specific instances of one single paradigm: reciprocal learning. These instances range from active learning over…
Towards Bayesian Data Selection
Julian Rodemann
A wide range of machine learning algorithms iteratively add data to the training sample. Examples include semi-supervised learning, active learning, multi-armed bandits, and Bayesi…
Statistical Multicriteria Benchmarking via the GSD-Front
Christoph Jansen, Georg Schollmeyer, Julian Rodemann +2
Given the vast number of classifiers that have been (and continue to be) proposed, reliable methods for comparing them are becoming increasingly important. The desire for reliabili…
Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision
Stefan Dietrich, Julian Rodemann, Christoph Jansen
We provide a theoretical and computational investigation of the Gamma-Maximin method with soft revision, which was recently proposed as a robust criterion for pseudo-label selectio…
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…