Julian Rodemann

Post-Doctoral Researcher in Statistics and Machine Learning

“Science is not a collection of facts, but a constant struggle with […] nature.” – attributed to Judea Pearl

“[…] and nature’s choice is usually entirely unknown to the statistician.” – Abraham Wald (1949): Statistical decision functions. Annals of Mathematical Statistics, 20:165–205. Page 173.

Thanks for your interest in my research.

I am a statistician working on reliable machine learning. In particular, I try to incorporate epistemic modeling uncertainty into adaptive machine learning methods such as self-training, superset learning or Bayesian optimization.

Research Interests

  • All things Bayesian
  • Imprecise Probabilities
    • Generalized Bayes
    • Imprecise Gaussian Processes
  • Optimization
  • Weak Supervision
  • Sampling Theory
  • … 1

Publications

Some Talks and Posters

  • Julian Rodemann (2025): Statistical Incentive-Aware AI Alignment. Stanford Trustworthy AI Research (STAIR) Lab, Stanford University, USA.
  • Julian Rodemann (2025): Imprecise Bayesian Optimization. Epistemic AI Group, TU Delft, Netherlands.
  • Julian Rodemann (2025): How Shapley Values Can Foster Human-AI Collaboration: A Case Study on Exosuit Personalization. Department of Computer Science, Universität des Saarlandes, Saarbrücken, Germany.
  • Julian Rodemann, Christoph Jansen, Georg Schollmeyer, James Bailie (2025): On Generalization from Self-Selected Samples. Second Workshop on Machine Learning under Weakly Structured Information, Tübingen AI Center, Germany.
  • Julian Rodemann (2025): Reciprocal Learning: A Unifying Perspective on Some Learning Paradigms. Department of Statistics, London School of Economics (LSE), London, United Kingdom.
  • Julian Rodemann, Christoph Jansen, Georg Schollmeyer, James Bailie (2025): Reciprocal Learning. DAGStat - Seventh Joint Statistical Meeting, Berlin, Germany.
  • Julian Rodemann, Christoph Jansen, Georg Schollmeyer (2025): Reciprocal Learning. DAGStat - Seventh Joint Statistical Meeting, Berlin, Germany.
  • Julian Rodemann (2025): Prior Misspecification in Bayesian Optimization. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany.
  • Julian Rodemann (2025): Semi-Supervised Learning Guided by the Generalized Bayes Rule. KleinLab, Karlsruher Institut für Technologie (KIT), Germany.
  • Julian Rodemann (2025): Reciprocal Learning. Explainable Machine Learning Group, Technical University of Munich (TUM) / Helmholtz Munich, Germany.
  • Julian Rodemann (2024): Reciprocal Learning. Optimization of Machine Learning Systems Group, University of Basel, Switzerland.
  • Julian Rodemann (2024): Beyond Static Machine Learning. Finnish Center for Artificial Intelligence (FCAI), Aalto University, Finland.
  • Julian Rodemann (2024): Reciprocal Learning. Department of Statistics, Harvard University, Cambridge, USA.
  • Julian Rodemann (2024): Reciprocal Learning. Seminar für Statistik (SfS), ETH Zürich, Switzerland.
  • Julian Rodemann (2024): Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. Integreat - Norwegian Centre for Knowledge-driven Machine Learning, University of Oslo & UiT Arctic University of Norway.
  • Julian Rodemann, Christoph Jansen, Georg Schollmeyer (2024): Can Reciprocal Learning Converge? First Workshop on Machine Learning under Weakly Structured Information, Munich, Germany.
  • Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Eyke Hüllermeier, Bernd Bischl, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio (2024): Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. Annual Summer Retreat, Department of Statistics, Herrsching, Germany.
  • Malte Nalenz, Julian Rodemann, Thomas Augustin (2023): De-Biased Regression Trees for Complex Data. Asian Conference on Machine Learning (ACML), Istanbul, Turkey.
  • Julian Rodemann, Thomas Augustin, Rianne de Heide (2023): Interpreting Generalized Bayesian Inference by Generalized Bayesian Inference. ISIPTA 2023
  • Alexander Marquardt, Julian Rodemann, Thomas Augustin (2023): An Empirical Study of Prior-Data Conflicts in Bayesian Neural Networks. ISIPTA 2023.
  • Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin (2023): Approximately Bayes-Optimal Pseudo Label Selection. Poster accepted for the Fifth Symposium on Advances in Approximate Bayesian Inference AABI, co-located with ICML 2023.
  • Julian Rodemann (2023): Learning Under Weak Supervision: Insights from Decision Theory. Young Statistician’s Lecture Series (YSLS). International Biometric Society (IBS) Early Career Working Group, Germany.
  • Malte Nalenz, Julian Rodemann, Thomas Augustin (2022): De-biased Regression Trees for Complex Data. Statistical Week by the German Statistical Society (DStatG) in Münster, Germany.
  • Julian Rodemann, Sebastian Fischer, Malte Nalenz, Lennart Schneider, Thomas Augustin (2022): Not All Data Are Created Equal - Lessons from Sampling Theory for Adaptive Machine Learning. Poster presented at ICSDS 2022 hosted by the Institute of Mathematical Statistics (IMS).
  • Julian Rodemann (2022): Prior-RObust Bayesian Optimization (PROBO). DAGStat Joint Statistical Meeting in Hamburg, Germany.
  • Julian Rodemann, Thomas Augustin (2022): A Deep Dive Into BO Sensitivity and PROBO. Young Statistician’s Lecture Series (YSLS). IBS Early Career Working Group, Germany.
  • Julian Rodemann, Dominik Kreiss, Thomas Augustin, Eyke Hüllermeier (2022): Levelwise Data Disambiguation. Annual Summer Retreat, Department of Statistics, Germany.
  • Julian Rodemann, Thomas Augustin (2021): Accounting for Imprecision of Model Specification in Bayesian Optimization. Poster presented at ISIPTA, Granada, Spain.
  • Julian Rodemann (2021): On Prior-Robustness in Bayesian Optimization. Annual Summer Retreat, Department of Statistics, Germany.
  • Julian Rodemann (2017): Clustering lifecycles. Villigst Machine Learning Undergraduate Workshop hosted by Max-Planck-Institute for Intelligent Systems (MPI-IS) Tübingen, Germany.

  1. “If we knew what it was we were doing, it would not be called research, would it?” – Albert Einstein ↩

  2. Equal Contribution. ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  3. This publication is based on parts of my master thesis “Robust stochastic derivative-free optimization”. ↩