The Care Label Concept: A Certification Suite for Trustworthy and Resource-Aware Machine Learning

no code implementations • 1 Jun 2021 • Katharina Morik, Helena Kotthaus, Lukas Heppe, Danny Heinrich, Raphael Fischer, Andreas Pauly, Nico Piatkowski

On the other hand, care labels are the result of a certification suite that tests whether stated guarantees hold.

BIG-bench Machine Learning

Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework

no code implementations • 21 May 2021 • Katharina Morik, Helena Kotthaus, Raphael Fischer, Sascha Mücke, Matthias Jakobs, Nico Piatkowski, Andreas Pauly, Lukas Heppe, Danny Heinrich

How can they be guaranteed for a certain implementation of a machine learning model?

BIG-bench Machine Learning Learning Theory

Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots

no code implementations • 29 Oct 2020 • Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras, Mario Morvan, Billy Edwards, Kai Hou Yip, Giovanna Tinetti, Subhajit Sarkar, James M. Dawson, Vadim Borisov, Gjergji Kasneci, Matej Petkovic, Tomaz Stepisnik, Tarek Al-Ubaidi, Rachel Louise Bailey, Michael Granitzer, Sahib Julka, Roman Kern, Patrick Ofner, Stefan Wagner, Lukas Heppe, Mirko Bunse, Katharina Morik

For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots.

BIG-bench Machine Learning

Resource-Constrained On-Device Learning by Dynamic Averaging

no code implementations • 25 Sep 2020 • Lukas Heppe, Michael Kamp, Linara Adilova, Danny Heinrich, Nico Piatkowski, Katharina Morik

This paper investigates an approach to communication-efficient on-device learning of integer exponential families that can be executed on low-power processors, is privacy-preserving, and effectively minimizes communication.

BIG-bench Machine Learning Privacy Preserving