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

Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
Revisiting Robustness in Graph Machine Learning
International Conference on Learning Representations (ICLR), 2023
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, and Stephan Günnemann
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
International Conference on Learning Representations (ICLR), 2022
Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann
Robustness of Graph Neural Networks at Scale
Neural Information Processing Systems (NeurIPS), 2021
Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
International Conference on Learning Representations (ICLR), 2021
Simon Geisler, Daniel Zügner, Stephan Günnemann
Reliable Graph Neural Networks via Robust Aggregation
Neural Information Processing Systems (NeurIPS), 2020
Aleksandar Bojchevski, Stephan Günnemann
Certifiable Robustness to Graph Perturbations
Neural Information Processing Systems (NeurIPS), 2019
Daniel Zügner, Stephan Günnemann
Certifiable Robustness and Robust Training for Graph Convolutional Networks
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
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