Faculty
Mario Fritz is a faculty at the CISPA Helmholtz Center for Information Security, an honorary professor at Saarland University, and a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS).
Until 2018, he led a research group at the Max Planck Institute for Computer Science.
Previously, he was a PostDoc at the International Computer Science Institute (ICSI) and UC Berkeley after receiving his PhD from TU Darmstadt and studying computer science at FAU Erlangen-Nuremberg.
He is currently coordinating and leading the EU funded network of excellence "ELSA - European Lighthouse on Secure and Safe AI", among other projects.
His research focuses on trustworthy artificial intelligence, especially at the intersection of information security and machine learning.
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches
Transactions on Machine Learning Research (TMLR) Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
GI International Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA)
Exploring the Potential of LLMs for Code Deobfuscation
Annual Meeting of the Association for Computational Linguistics (ACL)
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text Generation
International Conference on Machine Learning (ICML)
Stealix: Model Stealing via Prompt Evolution
Annual Meeting of the Association for Computational Linguistics (ACL)
A Theory of LLM Sampling: Part Descriptive and Part Prescriptive
International Conference on Machine Learning (ICML)
Pixel-level Certified Explanations via Randomized Smoothing