Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence

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Book Chapters


  • P. Scholl, F. Dietrich, C. Otte, and S. Udluft. Safe Policy Improvement Approachesand their limitations. In: Agents and Artificial Intelligence. Series: Lecture Notes in Artificial Intelligence. Springer. 2022 (arXiv:2208.00724).


  • S. Kolek, D. Nguyen, R. Levie, J. Bruna, and G. Kutyniok. A Rate-Distortion Framework for Explaining Black-box Model Decisions. In: xxAI - Beyond explainable Artificial Intelligence, to appear (arXiv:2110.08252).
  • J. Berner, P. Grohs, G. Kutyniok, and P. Petersen. The Modern Mathematics of Deep Learning. In: Mathematical Aspects of Deep Learning, Cambridge University Press, to appear (arXiv:2105.04026).


  • I. Gühring, M. Raslan, and G. Kutyniok. Expressivity of Deep Neural Networks. In: Mathematical Aspects of Deep Learning, to appear (arXiv:2007.04759).
  • G. Kutyniok. Shearlets: From Theory to Deep Learning. In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. K. Chen, C.-B. Schönlieb, X.-C. Tai, and L. Younes, Springer, to appear


  •  A. Flinth, A. Hashemi, and G. Kutyniok. Compressed Sensing: From Theory to Praxis. In: Compressive Sensing of Earth Observations, C.H. Chen, ed., Taylor and Francis 2017.
  • G. Kutyniok, M. März, and J. Ma. Mathematical Methods in Medical Image Processing. In: Quantification of Biophysical Parameters by Medical Imaging, I. Sack and T. Schäffter, eds., Springer 2017.


  • H. Boche, R. Calderbank, G. Kutyniok, and J. Vybiral. A Survey of Compressed Sensing. In: Compressed Sensing and its Applications, 1–40, Birkhäuser Boston, 2015. [pdf]


  • F. Sündermann, S. Lotter, W.-Q Lim, N. Golovyashkina, R. Brandt, and G. Kutyniok. Shearlet-analysis of cLSM images to extract morphological features of neurons. In: Laser Scanning Microscopy and Quantitative Image Analysis of Neuronal Tissue, L. Bakota, R. Brandt, eds., 293–304, Springer 2014.
  • W. Dahmen, C. Huang, G. Kutyniok, W.-Q Lim, C. Schwab, and G. Welper. Efficient Resolution of Anisotropic Structures. In: Extraction of Quantifiable Information from Complex Systems, 25–51, Springer, 2014.
  • F. Aurzada, A. Bley, A. Eisenblätter, H.-F. Geerdes, M. Guillemard, G. Kutyniok, F. Philipp, C. Rack, M. Scheutzow, and A. Werner. Mathematics for telecommunications. In: Matheon – Mathematics for Key Technologies, EMS Publishing House (2014), 75–89.


  • P. G. Casazza and G. Kutyniok. Fusion Frames. In: Finite Frames: Theory and Applications, 437–478, Birkhäuser Boston, 2012. [pdf]
  • P. G. Casazza, G. Kutyniok, and F. Philipp. Introduction to Finite Frame Theory. In: Finite Frames: Theory and Applications, 1–53, Birkhäuser Boston, 2012. [pdf]
  • G. Kutyniok. Data Separation by Sparse Representations. In: Compressed Sensing: Theory and Applications, 485–514, Cambridge University Press, 2012.
  • M. Davenport, M. Duarte, Y. Eldar, and G. Kutyniok. Introduction to Compressed Sensing. In: Compressed Sensing: Theory and Applications, 1–64, Cambridge University Press, 2012. [pdf]
  • G. Kutyniok and D. Labate. Introduction to Shearlets. In: Shearlets: Multiscale Analysis for Multivariate Data, 1–38, Birkhäuser Boston, 2012. [pdf]
  • G. Kutyniok, W.-Q Lim, and X. Zhuang. Digital Shearlet Transforms. In: Shearlets: Multiscale Analysis for Multivariate Data, 239–282, Birkhäuser Boston, 2012.
  • G. Kutyniok, J. Lemvig, and W.-Q Lim. Shearlets and Optimally Sparse Approximations. In: Shearlets: Multiscale Analysis for Multivariate Data, 145–198, Birkhäuser Boston, 2012.