Interpretable ML methods, Data modeling, Anomaly dection
The Distinguished Research Group of Prof. Dr. Klaus-Robert Müller is concentrating on the development of robust and interpretable machine learning methods for learning from complex structured and non-stationary data and the fusion of heterogeneous multi-modal data sources. A special focus lies on the efficient modeling of non-stationary, heterogeneous and structured data sources with deep learning and kernel methods. He and his team also work on theoretically sound incorporation of a priori knowledge from the application domain as well as the detection of anomalies in structured data. The resulting models are expected not only to be accurate, but also to explain their nonlinear decisions, quantify decision uncertainties, and create new knowledge about the studied data. In addition, Klaus-Robert Müller has been pursuing a long history of bringing machine learning into the sciences, which has helped to arrive at genuinely novel insights. In the last decade his attention has focused primarily on quantum chemistry, cancer research as well as computational neuroscience.
Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen
Morphological and molecular breast cancer profiling through explainable machine learning
Benjamin Blankertz, Ryota Tomioka, Steven Lemm, Motoaki Kawanabe, Klaus-Robert Müller
Optimizing spatial filters for robust eeg single-trial analysis
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller
A unifying review of deep and shallow anomaly detection
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld
Fast and accurate modeling of molecular atomization energies with machine learning
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