Accounting for Gaussian Process Imprecision in Bayesian Optimization. - Julian Rodemann; Thomas Augustin 001 (IUKM, 2022)

Levelwise Data Disambiguation by Cautious Superset Classification. - Julian Rodemann; Dominik Kreiss; Eyke Hüllermeier; Thomas Augustin 001 (SUM, 2022)

In all likelihoods: robust selection of pseudo-labeled data. - Julian Rodemann; Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (ISIPTA, 2023)

Robust statistical comparison of random variables with locally varying scale of measurement. - Christoph Jansen; Georg Schollmeyer; Hannah Blocher; Julian Rodemann; Thomas Augustin 001 (UAI, 2023)

Approximately Bayes-optimal pseudo-label selection. - Julian Rodemann; Jann Goschenhofer; Emilio Dorigatti; Thomas Nagler; Thomas Augustin 001 (UAI, 2023)

Approximate Bayes Optimal Pseudo-Label Selection. - Julian Rodemann; Jann Goschenhofer; Emilio Dorigatti; Thomas Nagler; Thomas Augustin 001 (CoRR, 2023)

In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning. - Julian Rodemann; Christoph Jansen; Georg Schollmeyer; Thomas Augustin 001 (CoRR, 2023)

Pseudo Label Selection is a Decision Problem. - Julian Rodemann (CoRR, 2023)

Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation. - Esteban Garces Arias; Julian Rodemann; Meimingwei Li; Christian Heumann; Matthias Aßenmacher (EMNLP (Findings), 2024)

Partial Rankings of Optimizers. - Julian Rodemann; Hannah Blocher (Tiny Papers @ ICLR, 2024)

Statistical Multicriteria Benchmarking via the GSD-Front. - Christoph Jansen; Georg Schollmeyer; Julian Rodemann; Hannah Blocher; Thomas Augustin 001 (NeurIPS, 2024)

Reciprocal Learning. - Julian Rodemann; Christoph Jansen; Georg Schollmeyer (NeurIPS, 2024)

Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. - Julian Rodemann; Federico Croppi; Philipp Arens; Yusuf Sale; Julia Herbinger; Bernd Bischl; Eyke Hüllermeier; Thomas Augustin 001; Conor J. Walsh; Giuseppe Casalicchio (CoRR, 2024)

Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision. - Stefan Dietrich; Julian Rodemann; Christoph Jansen (CoRR, 2024)

Towards Bayesian Data Selection. - Julian Rodemann (CoRR, 2024)

How to Choose a Reinforcement-Learning Algorithm. - Fabian Bongratz; Vladimir Golkov; Lukas Mautner; Luca Della Libera; Frederik Heetmeyer; Felix Czaja; Julian Rodemann; Daniel Cremers (CoRR, 2024)

Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework. - Esteban Garces Arias; Hannah Blocher; Julian Rodemann; Meimingwei Li; Christian Heumann; Matthias Aßenmacher (CoRR, 2024)

Imprecise Bayesian optimization. - Julian Rodemann; Thomas Augustin 001 (Knowl. Based Syst., 2024)

Learning de-biased regression trees and forests from complex samples. - Malte Nalenz; Julian Rodemann; Thomas Augustin 001 (Mach. Learn., 2024)

A Statistical Case Against Empirical Human-AI Alignment. - Julian Rodemann; Esteban Garces Arias; Christoph Luther; Christoph Jansen; Thomas Augustin 001 (CoRR, 2025)

Generalization Bounds and Stopping Rules for Learning with Self-Selected Data. - Julian Rodemann; James Bailie (CoRR, 2025)

Statistical Multicriteria Evaluation of LLM-Generated Text. - Esteban Garces Arias; Hannah Blocher; Julian Rodemann; Matthias Aßenmacher; Christoph Jansen (CoRR, 2025)