Yan Scholten

Technical University of Munich
Department of Computer Science - I26
Boltzmannstr. 3
85748 Garching b. München
Germany

Room: 00.11.065
E-Mail: y.scholten [at] tum.de
Website: yascho.github.io

Research Interests

• Reliable machine learning
• Conformal prediction
• Machine unlearning
• Machine learning on graphs

Selected Publications

Full list on Google Scholar

• Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
  Yan Scholten, Stephan Günnemann
  International Conference on Learning Representations, ICLR 2025 (Spotlight)
  [PDF | Project page | Code]
   
• A Probabilistic Perspective on Unlearning and Alignment for Large Language Models
  Yan Scholten, Stephan Günnemann, Leo Schwinn
  International Conference on Learning Representations, ICLR 2025 (Oral)
  [PDF | Project page | LLM framework (Code) | Confidence bounds (Code)]
   
• Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives
  Leo Schwinn, Yan Scholten, Tom Wollschläger, Sophie Xhonneux, Stephen Casper, 
  Stephan Günnemann, Gauthier Gidel
  [Preprint], 2025.
   
• Hierarchical Randomized Smoothing
  Yan Scholten, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
  Advances in Neural Information Processing Systems (NeurIPS), 2023
  [PDF | Project page | Code]
   
• (Provable) Adversarial Robustness for Group Equivariant Tasks:
  Graphs, Point Clouds, Molecules, and More
  Jan Schuchardt, Yan Scholten, Stephan Günnemann
  Advances in Neural Information Processing Systems (NeurIPS), 2023
  [PDF | Project page | Code]
   
• Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
  Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
  Advances in Neural Information Processing Systems (NeurIPS), 2022
  [ PDF | Project page | Code ]
   

Education

• 2022-now: PhD student in Computer Science, Technical University of Munich
• 2019-2022: M.Sc. Informatics - Technical University of Munich (passed with high distinction)
• 2015-2019: B.Sc. Computer Science (Math Minor) - Paderborn University (passed with distinction)

Academic Honors and Awards

• 2023: Admission to the Konrad Zuse School of Excellence in Reliable AI
• 2019: Deutschlandstipendium awarded by the Technical University of Munich
• 2018: RISE worldwide scholarship awarded by DAAD
• 2018: Deutschlandstipendium awarded by Studienfonds OWL
• 2017: Admission to elite program of the EIM-faculty at Paderborn University