Julian Rodemann

 Personal Website: www.julian-rodemann.de

Topics for Potential Theses

Topics for Bachelor/Master Theses under my supervision (see also my personal website):

• De-biased Boosting from Complex Samples
• Survey on Continual Learning (Literature research)
• Bayesian Optimization - a Decision-Theoretic Perspective

Teaching 

• Causality (Exercise, summer 23)
• Statistics for Geosciences (Exercise, winter 21/22, Lecture and exercise winter 22/23, winter 23/24)
• Wahrscheinlichkeitstheorie und Inferenz I (Probability Theory and Inference 1, exercise, winter 21/22)
• Wirtschafts- und Sozialstatistik (economic and social statistics, lecture/exercise, winter 21/22 and winter 22/23)
• Statistik IV im Nebenfach (statistics as minor IV, lecture/exercise, summer 22)
• Wahrscheinlichkeitstheoretische Grundlagen (basic probability theory, exercise, summer 22)

Research Interests

• Imprecise Probabilities
• Generalized Bayes
• Imprecise Gaussian Processes
• Neighborhood Models

• Robust Surrogates in Bayesian Optimization
• Superset Learning/Inference
• Weak Supervision
• (Causal Inference)

Publications

• Malte Nalenz, Julian Rodemann, Thomas Augustin (2024): Learning De-Biased Regression Trees and Forests from Complex Samples. Machine Learning, Special Issue of ACML 2023. Springer.
• Julian Rodemann (2023): Pseudo-Label Selection Is a Decision Problem (Short Paper). 46th German Conference on Artificial Intelligence, Berlin, Germany. LNAI, Springer.
• Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin (2023): Approximately Bayes-Optimal Pseudo-Label Selection. 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, USA. PMLR.
• Christoph Jansen, Georg Schollmeyer, Hannah Blocher, Julian Rodemann, Thomas Augustin (2023): Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement. 39th Conference on Uncertainty in Artificial Intelligence (UAI), Pittsburgh, USA. PMLR.
• Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin (2023): In All Likelihood(s): Robust Selection of Pseudo-Labeled Data. 13th International Symposium on Imprecise Probabilities: Theories and Applications (ISIPTA), Oviedo, Spain. PMLR.
• Julian Rodemann, Dominik Kreiss, Eyke Hüllermeier, Thomas Augustin (2022): Levelwise Data Disambiguation by Cautious Superset Classification. 15th International Conference on Scalable Uncertainty Management (SUM), Paris, France. LNAI, Springer.
• Julian Rodemann, Thomas Augustin (2022): Accounting for Gaussian Process Imprecision in Bayesian Optimization. 9th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM), Ishikawa, Japan. LNAI, Springer.

Talks

• 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

• Julian Rodemann, Thomas Augustin (2022): Towards Prior-Mean-Robust Bayesian Optimization. DAGStat - Sixth Joint Statistical Meeting. Hamburg, Germany. (Abstract)
• 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): Work In Progress: Levelwise Data Disambiguation by Cautious Superset Classification. Annual Summer Retreat, Department of Statistics.
• Julian Rodemann: Clustering lifecycles. Villigst Machine Learning Workshop hosted by Max-Planck-Institute
  (MPI) For Intelligent Systems in Tübingen, Germany.

Fachstudienberatung Bachelor (Hauptfach) Statistik und Data Science (PO 2021/2010)

Bitte vereinbaren Sie einen Termin per Mail oder stellen Ihre Fragen direkt: rodemann@stat.uni-muenchen.de

Research