Julian Rodemann

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

Teaching 

• Statistics for Geosciences (Übung, WS 21/22)
• Wahrscheinlichkeitstheorie und Inferenz I (Übung, WS 21/22)
• Wirtschafts- und Sozialstatistik (Vorlesung/Übung, WS 21/22)
• Statistik IV im Nebenfach (Vorlesung/Übung, SoSe 22)
• Wahrscheinlichkeitstheoretische Grundlagen (Übung, SoSe 22)

Research Interests

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

Publications

• Julian Rodemann, Thomas Augustin (2022): Accounting for Gaussian Process Imprecision in Bayesian Optimization. In: Proceedings of the Ninth International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM). Ishikawa, Japan. Lecture Notes on Artificial Intelligence, Springer.

Preprints

• Julian Rodemann, Christoph Jansen, Georg Schollmeyer, Thomas Augustin (2022): In All Likelihoods: How To Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning

Talks

• 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.

Research