Jan Schuchardt

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Stephan GünnemannProfessor of Computer Science, Technical University of MunichVerified email at in.tum.de
Aleksandar BojchevskiUniversity of CologneVerified email at uni-koeln.de
Simon GeislerTechnical University of MunichVerified email at in.tum.de
Yan ScholtenTechnical University of MunichVerified email at tum.de
Johannes Gasteiger, né KlicperaAnthropicVerified email at anthropic.com
Johanna SommerPruna AI, Technical University of MunichVerified email at in.tum.de
Vladimir GolkovTechnical University of MunichVerified email at tum.de
Daniel CremersTechnical University of MunichVerified email at tum.de
Morgane AyleTechnical University of MunichVerified email at tum.de
Lukas GoschPhD Student, Technical University of MunichVerified email at tum.de
Tom WollschlägerPhD Student in Machine Learning, TUMVerified email at tum.de
Daniel ZügnerMicrosoft ResearchVerified email at microsoft.com
Mihail StoianUniversity of Technology Nuremberg (UTN)Verified email at utn.de
Arthur KosmalaTechnical University of MunichVerified email at tum.de

Jan Schuchardt

Technical University of Munich

Verified email at in.tum.de - Homepage

Adversarial Robustness Graph Machine Learning Geometric Machine Learning Differential Privacy


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Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness S Geisler, J Sommer, J Schuchardt, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2022	47	2022
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks J Schuchardt, A Bojchevski, J Gasteiger, S Günnemann International Conference on Learning Representations (ICLR), 2021	38	2021
Randomized Message-Interception Smoothing: Gray-Box Certificates for Graph Neural Networks Y Scholten, J Schuchardt, S Geisler, A Bojchevski, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2022	22	2022
Learning to Evolve J Schuchardt, V Golkov, D Cremers arXiv preprint arXiv:1905.03389, 2019	10	2019
Localized Randomized Smoothing for Collective Robustness Certification J Schuchardt, T Wollschläger, A Bojchevski, S Günnemann International Conference on Learning Representations (ICLR), 2023	9	2023
Training Differentially Private Graph Neural Networks with Random Walk Sampling M Ayle, J Schuchardt, L Gosch, D Zügner, S Günnemann Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS, 2022	9	2022
Invariance-Aware Randomized Smoothing Certificates J Schuchardt, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2022	7	2022
Hierarchical Randomized Smoothing Y Scholten, J Schuchardt, A Bojchevski, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2023	6	2023
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More J Schuchardt, Y Scholten, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2023	4	2023
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification J Schuchardt, M Stoian, A Kosmala, S Günnemann Advances in Neural Information Processing Systems (NeurIPS), 2024	2	2024
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting J Schuchardt, M Dalirrooyfard, J Guzelkabaagac, A Schneider, ... arXiv preprint arXiv:2502.02410, 2025		2025

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