Welcome to the website of the Chair of Reliable Machine Learning

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The research group "Reliable Machine Learning" studies the properties of machine learning algorithms.
In view of the recent success of deep learning methods in applications like image recognition, speech recognition, and automatic translation, the group especially focuses on properties of deep neural networks.

Although a neural network trained e.g. for an image classification task might work well on "real inputs", it has been repeatedly shown empirically that such networks are vulnerable to adversarial examples:
a minimal perturbation (impercetible to a human) of the input data can cause the network to misclassify the input.
Thus, an important research area of the group is to mathematically understand the reasons for the existence of such adversarial examples (i.e., the instability of trained neural networks),
and - building on that understanding - to develop improved methods that yield provably robust neural networks.

The research group is supported by the Emmy Noether project "Stability and Solvability in Deep Learning".

Content of Chair of reliable machine learning

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[Translate to Englisch:] Konferenz Dubrovnik 2024

Multiscale conference in Dubrovnik

From March 19-22, 2024, Prof. Ray (MIDS) took part in the Multiscale Conference in Dubrovnik.

For the third time, mathematicians working on the…

[Translate to Englisch:] Pirmin Fontaine

MIDS welcomes Prof. Pirmin Fontaine as a new member of the Institute

The graduate mathematician completed his doctorate in logistics and supply chain management at the Technical University of Munich. As a postdoc, he…

Mathematical Institute for Machine Learning and Data Science


The Chair of Reliable Maschine Learning is part of the new founded Mathematical Institute for Machine Learning and Data Science, MIDS.
Learn more about MIDS here.