Robust Machine Learning

Topics: Robust & Reliable Machine Learning, Adversarial Machine Learning, Robust Data Analytics

In most real-world applications, the collected data is rarely of high-quality but often noisy, prone to errors, or vulnerable to manipulations. Corrupted sensors, errors in the measurement devices, or adversarial data manipulations are only a few examples. Standard machine learning and data analytics methods often fail in such scenarios. For example, even only slight deliberate perturbations of the input data (a.k.a. adversarial perturbations) can lead to dramatically different outcomes of the machine learning models. Such negative results significantly hinder the applicability of these models, leading to unintuitive and unreliable results, and they additionally open the door for attackers that can exploit these vulnerabilities. 

The goal of our research is to design robust machine learning techniques which handle various forms of errors/corruptions as well as changes in the underlying data distribution in an automatic way. Overall, this will lead to models that can be used in a reliable way, enabling their application even in sensitive application domains.

Selected Publications

  • Daniel Zügner, Stephan Günnemann
    Adversarial Attacks on Graph Neural Networks via Meta Learning
    International Conference on Learning Representations (ICLR), 2019
  • Richard Kurle, Stephan Günnemann, Patrick van der Smagt
    Multi-Source Neural Variational Inference
    AAAI Conference on Artificial Intelligence, 2019
  • Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
    Adversarial Attacks on Neural Networks for Graph Data (Best Research Paper Award)
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018
  • Richard Leibrandt, Stephan Günnemann
    Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation
    SIAM International Conference on Data Mining (SDM), 2018
  • Aleksandar Bojchevski, Stephan Günnemann
    Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure
    AAAI Conference on Artificial Intelligence, 2018
  • Aleksandar Bojchevski, Yves Matkovic, Stephan Günnemann
    Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017