Welcome...

...to the new website of the Machine Learning Group!

Latest News

  • Maksym Andriushchenko presented our NeurIPS 2019 paper "Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks" at the Swiss Machine Learning Day at EPFL and it got the best paper award - Congratulations, Maksym!
  • We present three papers at the NeurIPS Workshop Machine Learning with Guarantees
    • M. Andriushchenko, M. Hein: Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks - oral presentation
    • A. Meinke, M. Hein: Towards neural networks that provably know when they don't know
    • F. Croce, M. Hein: Provable robustness against all adversarial l_p-perturbations for p>=1
  • Two papers have been accepted at NeurIPS 2019:
    • M. Andriushchenko, M. Hein: Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. 
    • P. Mercado, F. Tudisco, M. Hein: Generalized Matrix Means for Semi-Supervised Learning and Multilayer Graphs.
  • Our work on Sparse and Imperceivable Adversarial Attacks has been accepted at ICCV 2019.
  • Our paper Error estimates for spectral convergence of the graph Laplacian on random geometric graphs towards the Laplace--Beltrami operator has been accepted at FOCM (Foundations of Computational Mathematics).
  • Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks has been accepted at IJCV.
  • Our new sparse and imperceivable white-box (PGD variant) and black-box attack (CornerSearch) has been accepted at ICCV 2019.
  • Our new fast adaptive boundary (fab-) attack improves upon the best reported results on the Madry robust CIFAR-10 network and for the robust TRADES model on MNIST and CIFAR-10.
  • Both our papers on Perron-Frobenius theory of multi-homogenous mappings and its application to tensor spectral problems got accepted at SIMAX. Congratulations, Antoine and Francesco!
  • Our CVPR paper Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem has been featured in the CVPR daily magazine
  • Our paper Spectral Clustering of Signed Graphs via Matrix Power Means has been accepted at ICML 2019.
  • Two of our papers have been accepted at CVPR 2019:
    • M. Hein, M. Andriushchenko, J. Bitterwolf (2019): Why ReLU networks yield high-confidence predeictions far away from the training data and how to mitigate the problem.
    • D. Stutz, M. Hein, B.Schiele (2019): Disentangling Adversarial Robustness and Generalization. 
  • Our paper Provable Robustness of ReLU networks via Maximization of Linear Regions has been accepted at AISTATS 2019.
  • Our paper On the loss landscape of a class of deep neural networks with no bad local valleys has been accepted at ICLR 2019.

Find these and more publications in the publications section!