EF1 – Extracting Dynamical Laws from Complex Data



Incorporating Locality into Fast(er) Learning

Project Heads

Nicole Mücke

Project Members

Abhishake Rastogi

Project Duration

01.10.2020 − 30.09.2022

Located at

TU Berlin


Classical stochastic approximation methods such as SGD in reproducing kernel Hilbert spaces are not able to exploit regions of different regularity of the target function. This slows down local convergence dramatically. To overcome this drawback, we propose to analyze localized SGD approaches.

Project Webpages

Selected Publications

[1] Nicole Mücke, Enrico Reiss, Stochastic Gradient Descent in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping, arXiv:2006.10840 

[2] Nicole MückeGergely NeuLorenzo RosascoBeating SGD Saturation with  Tail-Averaging and Minibatching, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 


[3] Nicole Mücke, Reducing training time by efficient localized kernel regression, Proceedings of Machine Learning Research, PMLR 89:2603-2610, 2019.


[4] Nicole Mücke, Stochastic Gradient Descent Meets Distribution Regression, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, California, USA. PMLR: Volume 130.


[5] Ernesto De VitoNicole MückeLorenzo RosascoReproducing kernel Hilbert spaces on manifolds: Sobolev and Diffusion spaces, Analysis and Applications 2020.


[6]  Nicole Mücke, Enrico Reiss, Jonas Rungenhagen, Markus Klein, Data splitting improves statistical performance in overparametrized regimes, arXiv:2110.10956

[7]  Abhishake Rastogi, and Peter Mathé, Inverse learning in Hilbert scales, arXiv preprint arXiv:2002.10208, 2020.

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