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Julian Rodemann

Imprecise Bayesian optimization
2024 · Julian Rodemann, Thomas Augustin · Knowledge-Based Systems · 33
Learning de-biased regression trees and forests from complex samples
2024 · Malte Nalenz, Julian Rodemann, et al. · Machine Learning · 18
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Accounting for Gaussian Process Imprecision in Bayesian Optimization
2022 · Julian Rodemann, Thomas Augustin · Lecture notes in computer science · 6
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation
2024 · Esteban Garces Arias, Julian Rodemann, et al. · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · 3
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Reciprocal Learning
2024 · Julian Rodemann, Christoph Jansen, et al. · neural information processing systems · 3
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Robust Generalizations of Stochastic Derivative-Free Optimization
2021 · Rodemann, Julian · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · 3
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Semi-supervised Learning Guided by the Generalized Bayes Rule Under Soft Revision
2024 · Stefan Dietrich, Julian Rodemann, et al. · Advances in intelligent systems and computing · 2
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Empirical decision theory
2026 · C. Jansen, G. Schollmeyer, et al. · Information Sciences · 1
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework
2024 · Arias, Esteban Garces, Blocher, Hannah, et al. · arXiv (Cornell University) · 1
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How to Choose a Reinforcement-Learning Algorithm
2024 · Bongratz, Fabian, Golkov, Vladimir, et al. · arXiv (Cornell University) · 1
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Towards Bayesian Data Selection
2024 · Rodemann, Julian · arXiv (Cornell University) · 1
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Pseudo Label Selection is a Decision Problem
2023 · Rodemann, Julian · arXiv (Cornell University) · 1
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Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement
2023 · Jansen, Christoph, Schollmeyer, Georg, et al. · arXiv (Cornell University) · 1
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Approximately Bayes-Optimal Pseudo Label Selection
2023 · Rodemann, Julian, Goschenhofer, Jann, et al. · arXiv (Cornell University) · 1
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Thomas Augustin’s contributions to imprecise probability and statistics
2026 · F. Coolen, F. Cozman, et al. · International Journal of Approximate Reasoning
Performative Learning Theory
2026 · Rodemann, Julian, Fischer-Abaigar, Unai, et al. · Figshare
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
2026 · Rodemann, Julian, Marquard, Alexander, et al. · Figshare
Off-Policy Evaluation with Strategic Agents via Local Disclosure
2026 · Vo, Kiet Q. H., Reddy, Abbavaram Gowtham, et al. · arXiv (Cornell University)
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Off-Policy Evaluation with Strategic Agents via Local Disclosure
2026 · Vo, Huynh Quang Kiet, Reddy, Abbavaram Gowtham, et al. · Figshare
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
2026 · Rodemann, Julian, Marquard, Alexander, et al. · arXiv (Cornell University)
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Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
2026 · Zhang, Chongsheng, Wang, Hao, et al. · arXiv (Cornell University)
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Quantification of Credal Uncertainty: A Distance-Based Approach
2026 · Gonzalez-Garcia, Xabier, Chau, Siu Lun, et al. · arXiv (Cornell University)
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Incentive Aware AI Regulations: A Credal Characterisation
2026 · Singh, Anurag, Rodemann, Julian, et al. · arXiv (Cornell University)
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Beyond Arrow: From Impossibility to Possibilities in Multi-Criteria Benchmarking
2026 · Gordienko, Polina, Jansen, Christoph, et al. · arXiv (Cornell University)
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Performative Learning Theory
2026 · Rodemann, Julian, Fischer-Abaigar, Unai, et al. · arXiv (Cornell University)
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Works count: 41
Citation count: 85
H-index: 4
I10-index: 2
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