26.06.2026
MCML at COLT 2026
Two Accepted Papers
39th Annual Conference on Learning Theory, San Diego, CA, USA, Jun 29-Jul 03, 2026
We are happy to announce that MCML researchers have contributed a total of 2 papers to COLT 2026. Congrats to our researchers!
Main Track (2 papers)
T. Nagler • S. Langer
Optimal neural network approximation of smooth compositional functions on sets with low intrinsic dimension.
COLT 2026 - 39th Annual Conference on Learning Theory. San Diego, CA, USA, Jun 29-Jul 03, 2026. URL
Optimal neural network approximation of smooth compositional functions on sets with low intrinsic dimension.
COLT 2026 - 39th Annual Conference on Learning Theory. San Diego, CA, USA, Jun 29-Jul 03, 2026. URL
A. Soleymani • B. Tahmasebi • P. Jaillet • S. Jegelka
Efficient Learning and Symmetry Discovery under Exact Invariances.
COLT 2026 - 39th Annual Conference on Learning Theory. San Diego, CA, USA, Jun 29-Jul 03, 2026. URL
Efficient Learning and Symmetry Discovery under Exact Invariances.
COLT 2026 - 39th Annual Conference on Learning Theory. San Diego, CA, USA, Jun 29-Jul 03, 2026. URL
Related
12.11.2026
One Toolbox for Many Kinds of Uncertainty
MCML researchers introduce probly, a unified Python package for uncertainty-aware machine learning, presented orally at NeurIPS 2026.
09.11.2026
Outstanding Paper Award at EMNLP 2026 for Work on Accent Bias in Audio Language Models
A paper co-authored by MCML PI Valentin Hofmann on accent bias in audio language models won an Outstanding Paper Award at EMNLP 2026.