Welcome to the Singular Learning Theory Days, a meeting on singular learning theory bringing together researchers from singularity theory, statistics, and machine learning.
The conference will take place at MPI CBG Dresden from October 26-27 2026.
About the meeting
Singular learning theory has developed into a rich framework for studying statistical models with singularities, yet work in the area remains dispersed across several communities. The workshop will bring these communities into sustained conversation.
The meeting has three main goals. First, it aims to connect and consolidate the singular learning theory community. Second, it will clarify what singular learning theory can contribute to present-day statistical and machine-learning practice, and which problems should shape the field over the next few years. Third, it will introduce researchers from complex and algebraic geometry to learning coefficients and related invariants, with the explicit aim of building a lasting bridge between these communities.
The programme contains research talks from leading scientists in the area, as well as time for collaborative working and open problem sessions to foster new collaborations.
Registration
The registration is open until September 1 2026. Please register here. There is limited funding available to support early-career researchers.
Invited Speakers
- Dan Bath (KU Leuven)
- Mathias Drton (Technical University of Munich)
- Anne Frühbis-Krüger (University of Oldenburg)
- Dimitra Kosta (University of Edinburgh)
- Edmund Lau (Department for Science, Innovation and Technology UK)
- Anthea Monod (Imperial College London)
- Jules Tsukahara (Sorbonne University)
- Sumio Watanabe (Institute of Science Tokyo) -online-
Organizers
- Jiayi Li (CSBD / MPI CBG / TU Dresden)
- Maximilian Wiesmann (CSBD / MPI CBG / MPI PKS / TU Dresden)
- Daniel Windisch (KU Leuven)
Funding
We gratefully acknowledge funding by the Max Planck Society, the Max Planck Institute of Molecular Cell Biology and Genetics, and by the DFG via the SPP 2458 Combinatorial Synergies.
