Scholar
Fabian Schaipp
Google Scholar ID: Wxb5J_8AAAAJ
Inria Paris
Citations & Impact
Citations
71
H-index
5
i10-index
4
Publications
11
Co-authors
12
list available
Publications
Resume (English only)
Academic Achievements
- Publications: ICML 2025 on understanding the behavior of learning-rate schedules for LLM training;
- Developed adaptive learning-rate methods based on the Polyak step size - MoMo (ICML 2024) and ProxSPS (TMLR 2023);
- Successfully defended PhD thesis.
Research Experience
- Since September 2024, working as a postdoc at Inria Paris;
- Visited CCM, Flatiron Institute, New York City in August 2023 and August 2022 as a visiting researcher.
Education
- PhD: Technical University of Munich (TUM), supervised by Professor Michael Ulbrich;
- Postdoc: Inria Paris, advised by Francis Bach, Umut Simsekli, and Adrien Taylor.
Background
- Research interests: Stochastic optimization algorithms for machine learning, and related topics in computation and statistics. Overview: Focuses on understanding and simplifying training recipes for ML models.
Miscellany
- Location: Paris
- Contact: firsstname[dot]lastname[at]tum[dot]de
