Simon Geisler

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 34

Top Topics

Top Venues

Publications

Zhong Li, Simon Geisler, Yuhang Wang, Stephan Günnemann, Matthijs van Leeuwen
Explainable Graph Neural Networks Under Fire. CoRR (2024)
Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
Relaxing Graph Transformers for Adversarial Attacks. CoRR (2024)
Simon Geisler, Tom Wollschläger, M. H. I. Abdalla, Johannes Gasteiger, Stephan Günnemann
Attacking Large Language Models with Projected Gradient Descent. CoRR (2024)
Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
Spatio-Spectral Graph Neural Networks. CoRR (2024)
Leo Schwinn, Simon Geisler
Revisiting the Robust Alignment of Circuit Breakers. CoRR (2024)
Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRR (2023)
Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
Revisiting Robustness in Graph Machine Learning. CoRR (2023)
Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
Revisiting Robustness in Graph Machine Learning. ICLR (2023)
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Adversarial Training for Graph Neural Networks. CoRR (2023)
Ege Erdogan, Simon Geisler, Stephan Günnemann
Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. CoRR (2023)
Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. CoRR (2023)
Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRL (2023)
Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
Transformers Meet Directed Graphs. ICML (2023)
Filippo Guerranti, Zinuo Yi, Anna Starovoit, Rafiq Kamel, Simon Geisler, Stephan Günnemann
On the Adversarial Robustness of Graph Contrastive Learning Methods. CoRR (2023)
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. NeurIPS (2023)
Simon Geisler, Yujia Li, Daniel J. Mankowitz, Ali Taylan Cemgil, Stephan Günnemann, Cosmin Paduraru
Transformers Meet Directed Graphs. CoRR (2023)
Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski
Are Defenses for Graph Neural Networks Robust? CoRR (2023)
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. ICLR (2022)
Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. NeurIPS (2022)
John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. ICML (2022)
John Rachwan, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, Stephan Günnemann
Winning the Lottery Ahead of Time: Efficient Early Network Pruning. CoRR (2022)
Simon Geisler, Carlos Cunha, Ravi Kumar Satzoda
Better, Faster Small Hazard Detection: Instance-Aware Techniques, Metrics and Benchmarking. IEEE Trans. Intell. Transp. Syst. 23 (7) (2022)
Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann
On the Robustness and Anomaly Detection of Sparse Neural Networks. CoRR (2022)
Felix Mujkanovic, Simon Geisler, Stephan Günnemann, Aleksandar Bojchevski
Are Defenses for Graph Neural Networks Robust? NeurIPS (2022)
Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. ICLR (2022)
Bertrand Charpentier, Oliver Borchert, Daniel Zügner, Simon Geisler, Stephan Günnemann
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. CoRR (2021)
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. CoRR (2021)
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. CoRR (2021)
Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
Robustness of Graph Neural Networks at Scale. CoRR (2021)
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, Stephan Günnemann
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. NeurIPS (2021)
Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
Robustness of Graph Neural Networks at Scale. NeurIPS (2021)
Simon Geisler, Daniel Zügner, Stephan Günnemann
Reliable Graph Neural Networks via Robust Aggregation. NeurIPS (2020)
Simon Geisler, Daniel Zügner, Stephan Günnemann
Reliable Graph Neural Networks via Robust Aggregation. CoRR (2020)
Julio De Melo Borges, Daniel Ziehr, Michael Beigl, Nélio Cacho, Allan de M. Martins, Adelson Araujo, Leonardo C. T. Bezerra, Simon Geisler
Time-Series Features for Predictive Policing. ISC2 (2018)