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ORCIDJulian Rodemann
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Journal Articles
- 2024
[j2]Julian Rodemann, Thomas Augustin:
Imprecise Bayesian optimization. Knowl. Based Syst. 300: 112186 (2024)- [j1]Malte Nalenz
, Julian Rodemann, Thomas Augustin:
Learning de-biased regression trees and forests from complex samples. Mach. Learn. 113(6): 3379-3398 (2024)
Conference and Workshop Papers
- 2024
- [c6]Julian Rodemann, Hannah Blocher:
Partial Rankings of Optimizers. Tiny Papers @ ICLR 2024 - 2023
- [c5]Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin:
In all likelihoods: robust selection of pseudo-labeled data. ISIPTA 2023: 412-425 - [c4]Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin:
Robust statistical comparison of random variables with locally varying scale of measurement. UAI 2023: 941-952 - [c3]Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin:
Approximately Bayes-optimal pseudo-label selection. UAI 2023: 1762-1773 - 2022
- [c2]Julian Rodemann, Thomas Augustin:
Accounting for Gaussian Process Imprecision in Bayesian Optimization. IUKM 2022: 92-104 - [c1]Julian Rodemann, Dominik Kreiss, Eyke Hüllermeier, Thomas Augustin:
Levelwise Data Disambiguation by Cautious Superset Classification. SUM 2022: 263-276
Informal and Other Publications
- 2024
- [i13]Julian Rodemann, Hannah Blocher:
Partial Rankings of Optimizers. CoRR abs/2402.16565 (2024) - [i12]Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio:
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. CoRR abs/2403.04629 (2024) - [i11]Stefan Dietrich, Julian Rodemann, Christoph Jansen:
Semi-Supervised Learning guided by the Generalized Bayes Rule under Soft Revision. CoRR abs/2405.15294 (2024) - [i10]Christoph Jansen, Georg Schollmeyer, Julian Rodemann, Hannah Blocher, Thomas Augustin:
Statistical Multicriteria Benchmarking via the GSD-Front. CoRR abs/2406.03924 (2024) - [i9]Julian Rodemann:
Towards Bayesian Data Selection. CoRR abs/2406.12560 (2024) - [i8]Esteban Garces Arias, Julian Rodemann, Meimingwei Li, Christian Heumann, Matthias Aßenmacher:
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation. CoRR abs/2407.18698 (2024) - [i7]Fabian Bongratz, Vladimir Golkov, Lukas Mautner, Luca Della Libera, Frederik Heetmeyer, Felix Czaja, Julian Rodemann, Daniel Cremers:
How to Choose a Reinforcement-Learning Algorithm. CoRR abs/2407.20917 (2024) - [i6]Julian Rodemann, Christoph Jansen, Georg Schollmeyer:
Reciprocal Learning. CoRR abs/2408.06257 (2024) - 2023
- [i5]Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin:
Approximate Bayes Optimal Pseudo-Label Selection. CoRR abs/2302.08883 (2023) - [i4]Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin:
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning. CoRR abs/2303.01117 (2023) - [i3]Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin:
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement. CoRR abs/2306.12803 (2023) - [i2]Julian Rodemann:
Pseudo Label Selection is a Decision Problem. CoRR abs/2309.13926 (2023) - 2021
- [i1]Julian Rodemann, Thomas Augustin:
Accounting for Gaussian Process Imprecision in Bayesian Optimization. CoRR abs/2111.08299 (2021)
Coauthor Index
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