High-Dimensional Indexing for Multimedia Features. - Ira Assent; Stephan Günnemann; Hardy Kremer; Thomas Seidl 001 (BTW, 2009)

Detection of orthogonal concepts in subspaces of high dimensional data. - Stephan Günnemann; Emmanuel Müller; Ines Färber; Thomas Seidl 001 (CIKM, 2009)

Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data. - Emmanuel Müller; Ira Assent; Stephan Günnemann; Ralph Krieger; Thomas Seidl 001 (ICDM, 2009)

DensEst: Density Estimation for Data Mining in High Dimensional Spaces. - Emmanuel Müller; Ira Assent; Ralph Krieger; Stephan Günnemann; Thomas Seidl 001 (SDM, 2009)

Evaluating Clustering in Subspace Projections of High Dimensional Data. - Emmanuel Müller; Stephan Günnemann; Ira Assent; Thomas Seidl 001 (Proc. VLDB Endow., 2009)

Pattern detector: fast detection of suspicious stream patterns for immediate reaction. - Ira Assent; Hardy Kremer; Stephan Günnemann; Thomas Seidl 001 (EDBT, 2010)

Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. - Stephan Günnemann; Ines Färber; Brigitte Boden; Thomas Seidl 001 (ICDM, 2010)

MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions. - Stephan Günnemann; Hardy Kremer; Ines Färber; Thomas Seidl 001 (ICDM Workshops, 2010)

Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques. - Hardy Kremer; Stephan Günnemann; Thomas Seidl 001 (ICDM Workshops, 2010)

Subgraph Mining on Directed and Weighted Graphs. - Stephan Günnemann; Thomas Seidl 001 (PAKDD (2), 2010)

Subspace Clustering for Uncertain Data. - Stephan Günnemann; Hardy Kremer; Thomas Seidl 001 (SDM, 2010)

MC-Tree: Improving Bayesian Anytime Classification. - Philipp Kranen; Stephan Günnemann; Sergej Fries; Thomas Seidl 001 (SSDBM, 2010)

CoDA: Interactive Cluster Based Concept Discovery. - Stephan Günnemann; Ines Färber; Hardy Kremer; Thomas Seidl 001 (Proc. VLDB Endow., 2010)

A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. - Emmanuel Müller; Ira Assent; Stephan Günnemann; Patrick Gerwert; Matthias Hannen; Timm Jansen; Thomas Seidl 001 (BTW, 2011)

External evaluation measures for subspace clustering. - Stephan Günnemann; Ines Färber; Emmanuel Müller; Ira Assent; Thomas Seidl 001 (CIKM, 2011)

Scalable density-based subspace clustering. - Emmanuel Müller; Ira Assent; Stephan Günnemann; Thomas Seidl 001 (CIKM, 2011)

Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations. - Stephan Günnemann; Hardy Kremer; Dominik Lenhard; Thomas Seidl 001 (EDBT, 2011)

Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. - Stephan Günnemann; Emmanuel Müller; Sebastian Raubach; Thomas Seidl 001 (ICDM, 2011)

Tracing Evolving Clusters by Subspace and Value Similarity. - Stephan Günnemann; Hardy Kremer; Charlotte Laufkötter; Thomas Seidl 001 (PAKDD (2), 2011)

DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors. - Stephan Günnemann; Brigitte Boden; Thomas Seidl 001 (ECML/PKDD (1), 2011)

Efficient Processing of Multiple DTW Queries in Time Series Databases. - Hardy Kremer; Stephan Günnemann; Anca Maria Ivanescu; Ira Assent; Thomas Seidl 001 (SSDBM, 2011)

Tracing clusters in evolving graphs with node attributes. - Brigitte Boden; Stephan Günnemann; Thomas Seidl 001 (CIKM, 2012)

Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. - Emmanuel Müller; Stephan Günnemann; Ines Färber; Thomas Seidl 001 (ICDE, 2012)

Assessing the Significance of Data Mining Results on Graphs with Feature Vectors. - Stephan Günnemann; Phuong Dao; Mohsen Jamali; Martin Ester (ICDM, 2012)

A Subspace Clustering Extension for the KNIME Data Mining Framework. - Stephan Günnemann; Hardy Kremer; Richard Musiol; Roman Haag; Thomas Seidl 001 (ICDM Workshops, 2012)

Effective and Robust Mining of Temporal Subspace Clusters. - Hardy Kremer; Stephan Günnemann; Arne Held; Thomas Seidl 001 (ICDM, 2012)

Mining coherent subgraphs in multi-layer graphs with edge labels. - Brigitte Boden; Stephan Günnemann; Holger Hoffmann; Thomas Seidl 001 (KDD, 2012)

Multi-view clustering using mixture models in subspace projections. - Stephan Günnemann; Ines Färber; Thomas Seidl 001 (KDD, 2012)

Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data. - Stephan Günnemann; Ines Färber; Kittipat Virochsiri; Thomas Seidl 001 (KDD, 2012)

Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases. - Hardy Kremer; Stephan Günnemann; Arne Held; Thomas Seidl 001 (PAKDD (1), 2012)

Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types. - Stephan Günnemann; Brigitte Boden; Thomas Seidl 001 (SSDBM, 2012)

Finding density-based subspace clusters in graphs with feature vectors. - Stephan Günnemann; Brigitte Boden; Thomas Seidl 001 (Data Min. Knowl. Discov., 2012)

Tracing Evolving Subspace Clusters in Temporal Climate Data. - Stephan Günnemann; Hardy Kremer; Charlotte Laufkötter; Thomas Seidl 001 (Data Min. Knowl. Discov., 2012)

Subspace clustering for complex data. - Stephan Günnemann (2012)

Subspace Clustering for Complex Data. - Stephan Günnemann (BTW, 2013)

Mixed Membership Subspace Clustering. - Stephan Günnemann; Christos Faloutsos (ICDM, 2013)

Spectral Subspace Clustering for Graphs with Feature Vectors. - Stephan Günnemann; Ines Färber; Sebastian Raubach; Thomas Seidl 001 (ICDM, 2013)

An Evaluation Framework for Temporal Subspace Clustering Approaches. - Hardy Kremer; Stephan Günnemann; Arne Held; Thomas Seidl 001 (ICDM Workshops, 2013)

Finding contexts of social influence in online social networks. - Jennifer H. Nguyen; Bo Hu 012; Stephan Günnemann; Martin Ester (SNAKDD, 2013)

Stochastic subspace search for top-k multi-view clustering. - Geng Li 002; Stephan Günnemann; Mohammed J. Zaki (MultiClust@KDD, 2013)

Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors. - Stephan Günnemann; Brigitte Boden; Ines Färber; Thomas Seidl 001 (PAKDD (1), 2013)

RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs. - Brigitte Boden; Stephan Günnemann; Holger Hoffmann; Thomas Seidl 001 (SSDBM, 2013)

Nesting the earth mover's distance for effective cluster tracing. - Hardy Kremer; Stephan Günnemann; Simon Wollwage; Thomas Seidl 001 (SSDBM, 2013)

SMVC: semi-supervised multi-view clustering in subspace projections. - Stephan Günnemann; Ines Färber; Matthias Sebastian Rüdiger; Thomas Seidl 001 (KDD, 2014)

Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution. - Stephan Günnemann; Nikou Günnemann; Christos Faloutsos (KDD, 2014)

Com2: Fast Automatic Discovery of Temporal ('Comet') Communities. - Miguel Araujo; Spiros Papadimitriou; Stephan Günnemann; Christos Faloutsos; Prithwish Basu; Ananthram Swami; Evangelos E. Papalexakis; Danai Koutra (PAKDD (2), 2014)

Fault-Tolerant Concept Detection in Information Networks. - Tobias Kötter; Stephan Günnemann; Michael R. Berthold; Christos Faloutsos (PAKDD (1), 2014)

Beyond Blocks: Hyperbolic Community Detection. - Miguel Araujo; Stephan Günnemann; Gonzalo Mateos; Christos Faloutsos (ECML/PKDD (1), 2014)

Robust multivariate autoregression for anomaly detection in dynamic product ratings. - Nikou Günnemann; Stephan Günnemann; Christos Faloutsos (WWW, 2014)

Linearized and Turbo Belief Propagation. - Wolfgang Gatterbauer; Stephan Günnemann; Danai Koutra; Christos Faloutsos (CoRR, 2014)

KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks. - Stephan Günnemann; Hardy Kremer; Matthias Hannen; Thomas Seidl 001 (CoRR, 2014)

GAMer: a synthesis of subspace clustering and dense subgraph mining. - Stephan Günnemann; Ines Färber; Brigitte Boden; Thomas Seidl 001 (Knowl. Inf. Syst., 2014)

Preferential Attachment in Graphs with Affinities. - Jay Lee; Manzil Zaheer; Stephan Günnemann; Alexander J. Smola (AISTATS, 2015)

Automatic Taxonomy Extraction from Bipartite Graphs. - Tobias Kötter; Stephan Günnemann; Michael R. Berthold; Christos Faloutsos (ICDM, 2015)

Effiziente Integration von Data- und Graph-Mining-Algorithmen in relationale Datenbanksysteme. - Manuel Then; Linnea Passing; Nina C. Hubig; Stephan Günnemann; Alfons Kemper; Thomas Neumann 001 (LWA, 2015)

Extracting Taxonomies from Bipartite Graphs. - Tobias Kötter; Stephan Günnemann; Christos Faloutsos; Michael R. Berthold (WWW (Companion Volume), 2015)

MultiClust special issue on discovering, summarizing and using multiple clusterings. - Emmanuel Müller; Ira Assent; Stephan Günnemann; Thomas Seidl 001; Jennifer G. Dy (Mach. Learn., 2015)

Linearized and Single-Pass Belief Propagation. - Wolfgang Gatterbauer; Stephan Günnemann; Danai Koutra; Christos Faloutsos (Proc. VLDB Endow., 2015)

Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques. - Saskia Metzler; Stephan Günnemann; Pauli Miettinen (ICDM, 2016)

EdgeCentric: Anomaly Detection in Edge-Attributed Networks. - Neil Shah; Alex Beutel; Bryan Hooi; Leman Akoglu; Stephan Günnemann; Disha Makhija; Mohit Kumar 008; Christos Faloutsos (ICDM Workshops, 2016)

Continuous Experience-aware Language Model. - Subhabrata Mukherjee; Stephan Günnemann; Gerhard Weikum (KDD, 2016)

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. - Bryan Hooi; Neil Shah; Alex Beutel; Stephan Günnemann; Leman Akoglu; Mohit Kumar 008; Disha Makhija; Christos Faloutsos (SDM, 2016)

Discovery of "comet" communities in temporal and labeled graphs Com^2. - Miguel Araujo; Stephan Günnemann; Spiros Papadimitriou; Christos Faloutsos; Prithwish Basu; Ananthram Swami; Evangelos E. Papalexakis; Danai Koutra (Knowl. Inf. Syst., 2016)

Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs. - Manuel Then; Stephan Günnemann; Alfons Kemper; Thomas Neumann 001 (BTW, 2017)

SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases. - Linnea Passing; Manuel Then; Nina C. Hubig; Harald Lang; Michael Schreier; Stephan Günnemann; Alfons Kemper; Thomas Neumann 001 (EDBT, 2017)

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings. - Aleksandar Bojchevski; Yves Matkovic; Stephan Günnemann (KDD, 2017)

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation. - Dhivya Eswaran; Stephan Günnemann; Christos Faloutsos (SDM, 2017)

Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data. - Nina C. Hubig; Philip Fengler; Andreas Züfle; Ruixin Yang; Stephan Günnemann (SSTD, 2017)

Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking. - Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2017)

Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion. - Subhabrata Mukherjee; Stephan Günnemann; Gerhard Weikum (CoRR, 2017)

Introduction to Tensor Decompositions and their Applications in Machine Learning. - Stephan Rabanser; Oleksandr Shchur; Stephan Günnemann (CoRR, 2017)

Machine Learning Meets Databases. - Stephan Günnemann (Datenbank-Spektrum, 2017)

Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs. - Manuel Then; Stephan Günnemann; Alfons Kemper; Thomas Neumann 001 (Datenbank-Spektrum, 2017)

MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. - Brigitte Boden; Stephan Günnemann; Holger Hoffmann; Thomas Seidl 001 (Knowl. Inf. Syst., 2017)

ZooBP: Belief Propagation for Heterogeneous Networks. - Dhivya Eswaran; Stephan Günnemann; Christos Faloutsos; Disha Makhija; Mohit Kumar 008 (Proc. VLDB Endow., 2017)

Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs. - Manuel Then; Timo Kersten; Stephan Günnemann; Alfons Kemper; Thomas Neumann 001 (Proc. VLDB Endow., 2017)

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure. - Aleksandar Bojchevski; Stephan Günnemann (AAAI, 2018)

Anomaly Detection in Car-Booking Graphs. - Oleksandr Shchur; Aleksandar Bojchevski; Mohamed Farghal; Stephan Günnemann; Yusuf Saber (ICDM Workshops, 2018)

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. - Aleksandar Bojchevski; Stephan Günnemann (ICLR (Poster), 2018)

NetGAN: Generating Graphs via Random Walks. - Aleksandar Bojchevski; Oleksandr Shchur; Daniel Zügner; Stephan Günnemann (ICML, 2018)

Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics. - Peter Wolf; Artur Mrowca; Tam Thanh Nguyen; Bernard Bäker; Stephan Günnemann (ITSC, 2018)

Adversarial Attacks on Neural Networks for Graph Data. - Daniel Zügner; Amir Akbarnejad; Stephan Günnemann (KDD, 2018)

ThingML+: Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning. - Armin Moin; Stephan Rössler; Stephan Günnemann (MoDELS (Workshops), 2018)

Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping. - Artur Mrowca; Barbara Moser; Stephan Günnemann (ECML/PKDD (3), 2018)

Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation. - Richard Leibrandt; Stephan Günnemann (SDM, 2018)

An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors. - Marawan Shalaby; Jan Stutzki; Matthias Schubert; Stephan Günnemann (SDM, 2018)

Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs. - Lorenzo von Ritter; Michael E. Houle; Stephan Günnemann (SISAP, 2018)

Dual-Primal Graph Convolutional Networks. - Federico Monti; Oleksandr Shchur; Aleksandar Bojchevski; Or Litany; Stephan Günnemann; Michael M. Bronstein (CoRR, 2018)

Adversarial Attacks on Node Embeddings. - Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2018)

Mining Contrasting Quasi-Clique Patterns. - Roberto Alonso; Stephan Günnemann (CoRR, 2018)

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank. - Johannes Klicpera; Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2018)

Pitfalls of Graph Neural Network Evaluation. - Oleksandr Shchur; Maximilian Mumme; Aleksandar Bojchevski; Stephan Günnemann (CoRR, 2018)

Multi-Source Neural Variational Inference. - Richard Kurle; Stephan Günnemann; Patrick van der Smagt (AAAI, 2019)

In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems. - Maximilian E. Schüle; Frédéric Simonis; Thomas Heyenbrock; Alfons Kemper; Stephan Günnemann; Thomas Neumann 001 (BTW, 2019)

Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference. - Artur Mrowca; Martin Nocker; Sebastian Steinhorst; Stephan Günnemann (DAC, 2019)

ML2SQL - Compiling a Declarative Machine Learning Language to SQL and Python. - Maximilian E. Schüle; Matthias Bungeroth; Dimitri Vorona; Alfons Kemper; Stephan Günnemann; Thomas Neumann 001 (EDBT, 2019)

The Power of SQL Lambda Functions. - Maximilian E. Schüle; Dimitri Vorona; Linnea Passing; Harald Lang; Alfons Kemper; Stephan Günnemann; Thomas Neumann 001 (EDBT, 2019)

Adversarial Attacks on Graph Neural Networks. - Daniel Zügner; Amir Akbarnejad; Stephan Günnemann (GI-Jahrestagung, 2019)

Predict then Propagate: Graph Neural Networks meet Personalized PageRank. - Johannes Klicpera; Aleksandar Bojchevski; Stephan Günnemann (ICLR (Poster), 2019)

Adversarial Attacks on Graph Neural Networks via Meta Learning. - Daniel Zügner; Stephan Günnemann (ICLR (Poster), 2019)

Adversarial Attacks on Node Embeddings via Graph Poisoning. - Aleksandar Bojchevski; Stephan Günnemann (ICML, 2019)

Certifiable Robustness and Robust Training for Graph Convolutional Networks. - Daniel Zügner; Stephan Günnemann (KDD, 2019)

Certifiable Robustness to Graph Perturbations. - Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2019)

Uncertainty on Asynchronous Time Event Prediction. - Bertrand Charpentier; Marin Bilos; Stephan Günnemann (NeurIPS, 2019)

Diffusion Improves Graph Learning. - Johannes Klicpera; Stefan Weißenberger; Stephan Günnemann (NeurIPS, 2019)

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift. - Stephan Rabanser; Stephan Günnemann; Zachary C. Lipton (NeurIPS, 2019)

MLearn: A Declarative Machine Learning Language for Database Systems. - Maximilian E. Schüle; Matthias Bungeroth; Alfons Kemper; Stephan Günnemann; Thomas Neumann 001 (DEEM@SIGMOD, 2019)

GhostLink: Latent Network Inference for Influence-aware Recommendation. - Subhabrata Mukherjee; Stephan Günnemann (WWW, 2019)

Overlapping Community Detection with Graph Neural Networks. - Oleksandr Shchur; Stephan Günnemann (CoRR, 2019)

Oktoberfest Food Dataset. - Alexander Ziller; Julius Hansjakob; Vitalii Rusinov; Daniel Zügner; Peter Vogel; Stephan Günnemann (CoRR, 2019)

Stability and dynamics of communities on online question-answer sites. - Saskia Metzler; Stephan Günnemann; Pauli Miettinen (Soc. Networks, 2019)

Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs. - Zhen Han 003; Yunpu Ma; Yuyi Wang 001; Stephan Günnemann; Volker Tresp (AKBC, 2020)

Group Centrality Maximization for Large-scale Graphs. - Eugenio Angriman; Alexander van der Grinten; Aleksandar Bojchevski; Daniel Zügner; Stephan Günnemann; Henning Meyerhenke (ALENEX, 2020)

Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection. - Felippe Schmoeller Roza; Maximilian Henne; Karsten Roscher; Stephan Günnemann (ECCV Workshops (6), 2020)

Directional Message Passing for Molecular Graphs. - Johannes Klicpera; Janek Groß; Stephan Günnemann (ICLR, 2020)

Continual Learning with Bayesian Neural Networks for Non-Stationary Data. - Richard Kurle; Botond Cseke; Alexej Klushyn; Patrick van der Smagt; Stephan Günnemann (ICLR, 2020)

Intensity-Free Learning of Temporal Point Processes. - Oleksandr Shchur; Marin Bilos; Stephan Günnemann (ICLR, 2020)

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. - Aleksandar Bojchevski; Johannes Klicpera; Stephan Günnemann (ICML, 2020)

Scaling Graph Neural Networks with Approximate PageRank. - Aleksandar Bojchevski; Johannes Klicpera; Bryan Perozzi; Amol Kapoor; Martin Blais; Benedek Rózemberczki; Michal Lukasik; Stephan Günnemann (KDD, 2020)

Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. - Daniel Zügner; Stephan Günnemann (KDD, 2020)

From things' modeling language (ThingML) to things' machine learning (ThingML2). - Armin Moin; Stephan Rössler; Marouane Sayih; Stephan Günnemann (MoDELS (Companion), 2020)

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts. - Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2020)

Reliable Graph Neural Networks via Robust Aggregation. - Simon Geisler; Daniel Zügner; Stephan Günnemann (NeurIPS, 2020)

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. - Richard Kurle; Syama Sundar Rangapuram; Emmanuel de Bézenac; Stephan Günnemann; Jan Gasthaus (NeurIPS, 2020)

Fast and Flexible Temporal Point Processes with Triangular Maps. - Oleksandr Shchur; Nicholas Gao; Marin Bilos; Stephan Günnemann (NeurIPS, 2020)

Gauss Shift: Density Attractor Clustering Faster Than Mean Shift. - Richard Leibrandt; Stephan Günnemann (ECML/PKDD (1), 2020)

Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs. - Zhen Han 003; Yuyi Wang 001; Yunpu Ma; Stephan Günnemann; Volker Tresp (CoRR, 2020)

Scene Graph Reasoning for Visual Question Answering. - Marcel Hildebrandt; Hang Li 010; Rajat Koner; Volker Tresp; Stephan Günnemann (CoRR, 2020)

Deep Representation Learning and Clustering of Traffic Scenarios. - Nick Harmening; Marin Bilos; Stephan Günnemann (CoRR, 2020)

Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training. - Anna-Kathrin Kopetzki; Stephan Günnemann (CoRR, 2020)

Equivariant Normalizing Flows for Point Processes and Sets. - Marin Bilos; Stephan Günnemann (CoRR, 2020)

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. - Johannes Klicpera; Shankari Giri; Johannes T. Margraf; Stephan Günnemann (CoRR, 2020)

Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns. - Daniel Zügner; Oliver Borchert; Amir Akbarnejad; Stephan Günnemann (ACM Trans. Knowl. Discov. Data, 2020)

Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. - Yihan Wu; Aleksandar Bojchevski; Aleksei Kuvshinov; Stephan Günnemann (AISTATS, 2021)

OODformer: Out-Of-Distribution Detection Transformer. - Rajat Koner; Poulami Sinhamahapatra; Karsten Roscher; Stephan Günnemann; Volker Tresp (BMVC, 2021)

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks. - Jan Schuchardt; Aleksandar Bojchevski; Johannes Klicpera; Stephan Günnemann (ICLR, 2021)

Language-Agnostic Representation Learning of Source Code from Structure and Context. - Daniel Zügner; Tobias Kirschstein; Michele Catasta; Jure Leskovec; Stephan Günnemann (ICLR, 2021)

Scalable Normalizing Flows for Permutation Invariant Densities. - Marin Bilos; Stephan Günnemann (ICML, 2021)

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More. - Johannes Klicpera; Marten Lienen; Stephan Günnemann (ICML, 2021)

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? - Anna-Kathrin Kopetzki; Bertrand Charpentier; Daniel Zügner; Sandhya Giri; Stephan Günnemann (ICML, 2021)

Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments. - Tom Haider; Felippe Schmoeller Roza; Dirk Eilers; Karsten Roscher; Stephan Günnemann (AISafety@IJCAI, 2021)

Neural Temporal Point Processes: A Review. - Oleksandr Shchur; Ali Caner Türkmen; Tim Januschowski; Stephan Günnemann (IJCAI, 2021)

Neural Flows: Efficient Alternative to Neural ODEs. - Marin Bilos; Johanna Sommer; Syama Sundar Rangapuram; Tim Januschowski; Stephan Günnemann (NeurIPS, 2021)

GemNet: Universal Directional Graph Neural Networks for Molecules. - Johannes Gasteiger; Florian Becker; Stephan Günnemann (NeurIPS, 2021)

Directional Message Passing on Molecular Graphs via Synthetic Coordinates. - Johannes Gasteiger; Chandan Yeshwanth; Stephan Günnemann (NeurIPS, 2021)

Robustness of Graph Neural Networks at Scale. - Simon Geisler; Tobias Schmidt; Hakan Sirin; Daniel Zügner; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2021)

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience. - Johannes C. Paetzold; Julian McGinnis; Suprosanna Shit; Ivan Ezhov; Paul Büschl; Chinmay Prabhakar; Anjany Sekuboyina; Mihail I. Todorov; Georgios Kaissis; Ali Ertürk; Stephan Günnemann; Bjoern H. Menze (NeurIPS Datasets and Benchmarks, 2021)

Detecting Anomalous Event Sequences with Temporal Point Processes. - Oleksandr Shchur; Ali Caner Türkmen; Tim Januschowski; Jan Gasthaus; Stephan Günnemann (NeurIPS, 2021)

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification. - Maximilian Stadler; Bertrand Charpentier; Simon Geisler; Daniel Zügner; Stephan Günnemann (NeurIPS, 2021)

Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering. - Rajat Koner; Hang Li 010; Marcel Hildebrandt; Deepan Das; Volker Tresp; Stephan Günnemann (ISWC, 2021)

In-Database Machine Learning with SQL on GPUs. - Maximilian E. Schüle; Harald Lang; Maximilian Springer; Alfons Kemper; Thomas Neumann 001; Stephan Günnemann (SSDBM, 2021)

A Model-Driven Engineering Approach to Machine Learning and Software Modeling. - Armin Moin; Atta Badii; Stephan Günnemann (CoRR, 2021)

Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications. - Armin Moin; Atta Badii; Stephan Günnemann (CoRR, 2021)

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. - Armin Moin; Andrei Mituca; Atta Badii; Stephan Günnemann (CoRR, 2021)

MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence. - Armin Moin; Moharram Challenger; Atta Badii; Stephan Günnemann (CoRR, 2021)

On Out-of-distribution Detection with Energy-based Models. - Sven Elflein; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (CoRR, 2021)

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph). - Johannes C. Paetzold; Julian McGinnis; Suprosanna Shit; Ivan Ezhov; Paul Büschl; Chinmay Prabhakar; Mihail I. Todorov; Anjany Sekuboyina; Georgios Kaissis; Ali Ertürk; Stephan Günnemann; Bjoern H. Menze (CoRR, 2021)

On Second-order Optimization Methods for Federated Learning. - Sebastian Bischoff 001; Stephan Günnemann; Martin Jaggi; Sebastian U. Stich (CoRR, 2021)

A Study of Joint Graph Inference and Forecasting. - Daniel Zügner; François-Xavier Aubet; Victor Garcia Satorras; Tim Januschowski; Stephan Günnemann; Jan Gasthaus (CoRR, 2021)

Mining communities and their descriptions on attributed graphs: a survey. - Martin Atzmueller; Stephan Günnemann; Albrecht Zimmermann (Data Min. Knowl. Discov., 2021)

Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training. - Anna-Kathrin Kopetzki; Stephan Günnemann (Mach. Learn., 2021)

Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks. - Aleksei Kuvshinov; Daniel Knobloch; Daniel Külzer; Elen Vardanyan; Stephan Günnemann (AAAI, 2022)

Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space. - Poulami Sinhamahapatra; Rajat Koner; Karsten Roscher; Stephan Günnemann (SafeAI@AAAI, 2022)

Supporting AI Engineering on the IoT Edge through Model-Driven TinyML. - Armin Moin; Moharram Challenger; Atta Badii; Stephan Günnemann (COMPSAC, 2022)

Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model. - Codrut-Andrei Diaconu; Sudipan Saha; Stephan Günnemann; Xiao Xiang Zhu 001 (CVPR Workshops, 2022)

Towards Model-Driven Engineering for Quantum AI. - Armin Moin; Moharram Challenger; Atta Badii; Stephan Günnemann (GI-Jahrestagung, 2022)

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions. - Bertrand Charpentier; Oliver Borchert; Daniel Zügner; Simon Geisler; Stephan Günnemann (ICLR, 2022)

Differentiable DAG Sampling. - Bertrand Charpentier; Simon Kibler; Stephan Günnemann (ICLR, 2022)

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions. - Nicholas Gao; Stephan Günnemann (ICLR, 2022)

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness. - Simon Geisler; Johanna Sommer; Jan Schuchardt; Aleksandar Bojchevski; Stephan Günnemann (ICLR, 2022)

Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. - Marten Lienen; Stephan Günnemann (ICLR, 2022)

End-to-End Learning of Probabilistic Hierarchies on Graphs. - Daniel Zügner; Bertrand Charpentier; Morgane Ayle; Sascha Geringer; Stephan Günnemann (ICLR, 2022)

Winning the Lottery Ahead of Time: Efficient Early Network Pruning. - John Rachwan; Daniel Zügner; Bertrand Charpentier; Simon Geisler; Morgane Ayle; Stephan Günnemann (ICML, 2022)

3D Infomax improves GNNs for Molecular Property Prediction. - Hannes Stärk; Dominique Beaini; Gabriele Corso; Prudencio Tossou; Christian Dallago; Stephan Günnemann; Pietro Lió (ICML, 2022)

Intriguing Properties of Input-Dependent Randomized Smoothing. - Peter Súkeník; Aleksei Kuvshinov; Stephan Günnemann (ICML, 2022)

Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning. - Felippe Schmoeller Roza; Hassan Rasheed; Karsten Roscher; Xiangyu Ning; Stephan Günnemann (ICMLA, 2022)

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services. - Armin Moin; Andrei Mituca; Moharram Challenger; Atta Badii; Stephan Günnemann (ICSE-Companion, 2022)

Influence-Based Mini-Batching for Graph Neural Networks. - Johannes Gasteiger; Chendi Qian; Stephan Günnemann (LoG, 2022)

A Systematic Evaluation of Node Embedding Robustness. - Alexandru Cristian Mara; Jefrey Lijffijt; Stephan Günnemann; Tijl De Bie (LoG, 2022)

MDE for machine learning-enabled software systems: a case study and comparison of MontiAnna & ML-Quadrat. - Jörg Christian Kirchhof; Evgeny Kusmenko; Jonas Ritz; Bernhard Rumpe; Armin Moin; Atta Badii; Stephan Günnemann; Moharram Challenger (MoDELS (Companion), 2022)

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution. - Leon Hetzel; Simon Böhm; Niki Kilbertus; Stephan Günnemann; Mohammad Lotfollahi; Fabian J. Theis (NeurIPS, 2022)

Are Defenses for Graph Neural Networks Robust? - Felix Mujkanovic; Simon Geisler; Stephan Günnemann; Aleksandar Bojchevski (NeurIPS, 2022)

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks. - Yan Scholten; Jan Schuchardt; Simon Geisler; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2022)

Invariance-Aware Randomized Smoothing Certificates. - Jan Schuchardt; Stephan Günnemann (NeurIPS, 2022)

Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm. - Nicola Franco; Tom Wollschläger; Nicholas Gao; Jeanette Miriam Lorenz; Stephan Günnemann (QCE, 2022)

Multi-Objective Model Selection for Time Series Forecasting. - Oliver Borchert; David Salinas; Valentin Flunkert; Tim Januschowski; Stephan Günnemann (CoRR, 2022)

Graph Data Augmentation for Graph Machine Learning: A Survey. - Tong Zhao 003; Gang Liu 025; Stephan Günnemann; Meng Jiang 001 (CoRR, 2022)

Enabling Automated Machine Learning for Model-Driven AI Engineering. - Armin Moin; Ukrit Wattanavaekin; Alexandra Lungu; Moharram Challenger; Atta Badii; Stephan Günnemann (CoRR, 2022)

How Do Graph Networks Generalize to Large and Diverse Molecular Systems? - Johannes Gasteiger; Muhammed Shuaibi; Anuroop Sriram; Stephan Günnemann; Zachary W. Ulissi; C. Lawrence Zitnick; Abhishek Das (CoRR, 2022)

Predicting single-cell perturbation responses for unseen drugs. - Leon Hetzel; Simon Böhm; Niki Kilbertus; Stephan Günnemann; Mohammad Lotfollahi; Fabian J. Theis (CoRR, 2022)

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning. - Bertrand Charpentier; Ransalu Senanayake; Mykel J. Kochenderfer; Stephan Günnemann (CoRR, 2022)

On the Robustness and Anomaly Detection of Sparse Neural Networks. - Morgane Ayle; Bertrand Charpentier; John Rachwan; Daniel Zügner; Simon Geisler; Stephan Günnemann (CoRR, 2022)

United States Politicians' Tone Became More Negative with 2016 Primary Campaigns. - Jonathan Külz; Andreas Spitz; Ahmad Abu-Akel; Stephan Günnemann; Robert West 001 (CoRR, 2022)

Irregularly-Sampled Time Series Modeling with Spline Networks. - Marin Bilos; Emanuel Ramneantu; Stephan Günnemann (CoRR, 2022)

torchode: A Parallel ODE Solver for PyTorch. - Marten Lienen; Stephan Günnemann (CoRR, 2022)

Modeling Temporal Data as Continuous Functions with Process Diffusion. - Marin Bilos; Kashif Rasul; Anderson Schneider; Yuriy Nevmyvaka; Stephan Günnemann (CoRR, 2022)

Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132). - Martin Grohe; Stephan Günnemann; Stefanie Jegelka; Christopher Morris 001 (Dagstuhl Reports, 2022)

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation. - Artur Mrowca; Florian Gyrock; Stephan Günnemann (Data Min. Knowl. Discov., 2022)

Recursive SQL and GPU-support for in-database machine learning. - Maximilian E. Schüle; Harald Lang; Maximilian Springer; Alfons Kemper; Thomas Neumann 001; Stephan Günnemann (Distributed Parallel Databases, 2022)

Robustness verification of ReLU networks via quadratic programming. - Aleksei Kuvshinov; Stephan Günnemann (Mach. Learn., 2022)

A model-driven approach to machine learning and software modeling for the IoT. - Armin Moin; Moharram Challenger; Atta Badii; Stephan Günnemann (Softw. Syst. Model., 2022)

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets. - Johannes Gasteiger; Muhammed Shuaibi; Anuroop Sriram; Stephan Günnemann; Zachary W. Ulissi; C. Lawrence Zitnick; Abhishek Das (Trans. Mach. Learn. Res., 2022)

C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data. - Kevin Kennard Thiel; Florian Naumann; Eduard Jundt; Stephan Günnemann; Gudrun Klinker (IEEE Trans. Vis. Comput. Graph., 2022)

How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? - Sina Stocker; Johannes Gasteiger; Florian Becker; Stephan Günnemann; Johannes T. Margraf (Mach. Learn. Sci. Technol., 2022)

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution. - Nicola Franco; Daniel Korth; Jeanette Miriam Lorenz; Karsten Roscher; Stephan Günnemann (AISafety/SafeRL@IJCAI, 2023)

Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models. - Tom Haider; Karsten Roscher; Felippe Schmoeller da Roza; Stephan Günnemann (AAMAS, 2023)

Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss. - Sebastian Schmidt 006; Stephan Günnemann (BMVC, 2023)

Enabling Machine Learning in Software Architecture Frameworks. - Armin Moin; Atta Badii; Stephan Günnemann; Moharram Challenger (CAIN, 2023)

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. - Jianxiang Feng; Jongseok Lee; Simon Geisler; Stephan Günnemann; Rudolph Triebel (CoRL, 2023)

Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. - Leo Schwinn; David Dobre; Stephan Günnemann; Gauthier Gidel (ICBINB, 2023)

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks. - Nicholas Gao; Stephan Günnemann (ICLR, 2023)

Revisiting Robustness in Graph Machine Learning. - Lukas Gosch; Daniel Sturm 002; Simon Geisler; Stephan Günnemann (ICLR, 2023)

Unveiling the sampling density in non-uniform geometric graphs. - Raffaele Paolino; Aleksandar Bojchevski; Stephan Günnemann; Gitta Kutyniok; Ron Levie (ICLR, 2023)

Localized Randomized Smoothing for Collective Robustness Certification. - Jan Schuchardt; Tom Wollschläger; Aleksandar Bojchevski; Stephan Günnemann (ICLR, 2023)

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. - Marin Bilos; Kashif Rasul; Anderson Schneider; Yuriy Nevmyvaka; Stephan Günnemann (ICML, 2023)

Generalizing Neural Wave Functions. - Nicholas Gao; Stephan Günnemann (ICML, 2023)

Transformers Meet Directed Graphs. - Simon Geisler; Yujia Li 001; Daniel J. Mankowitz; Ali Taylan Cemgil; Stephan Günnemann; Cosmin Paduraru (ICML, 2023)

Ewald-based Long-Range Message Passing for Molecular Graphs. - Arthur Kosmala; Johannes Gasteiger; Nicholas Gao; Stephan Günnemann (ICML, 2023)

Uncertainty Estimation for Molecules: Desiderata and Methods. - Tom Wollschläger; Nicholas Gao; Bertrand Charpentier; Mohamed Amine Ketata; Stephan Günnemann (ICML, 2023)

Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework. - Franziska Schwaiger; Andrea Matic; Karsten Roscher; Stephan Günnemann (IV, 2023)

Edge Directionality Improves Learning on Heterophilic Graphs. - Emanuele Rossi; Bertrand Charpentier; Francesco Di Giovanni; Fabrizio Frasca; Stephan Günnemann; Michael M. Bronstein (LoG, 2023)

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions. - Lukas Gosch; Simon Geisler; Daniel Sturm 002; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2023)

Add and Thin: Diffusion for Temporal Point Processes. - David Lüdke; Marin Bilos; Oleksandr Shchur; Marten Lienen; Stephan Günnemann (NeurIPS, 2023)

Hierarchical Randomized Smoothing. - Yan Scholten; Jan Schuchardt; Aleksandar Bojchevski; Stephan Günnemann (NeurIPS, 2023)

(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More. - Jan Schuchardt; Yan Scholten; Stephan Günnemann (NeurIPS, 2023)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (ECML/PKDD (1), 2023)

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness. - Nicola Franco; Tom Wollschläger; Benedikt Poggel; Stephan Günnemann; Jeanette Miriam Lorenz (QCE, 2023)

Training Differentially Private Graph Neural Networks with Random Walk Sampling. - Morgane Ayle; Jan Schuchardt; Lukas Gosch; Daniel Zügner; Stephan Günnemann (CoRR, 2023)

Training, Architecture, and Prior for Deterministic Uncertainty Methods. - Bertrand Charpentier; Chenxiang Zhang; Stephan Günnemann (CoRR, 2023)

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection. - Nicola Franco; Daniel Korth; Jeanette Miriam Lorenz; Karsten Roscher; Stephan Günnemann (CoRR, 2023)

Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models. - Johannes Getzner; Bertrand Charpentier; Stephan Günnemann (CoRR, 2023)

MAGNet: Motif-Agnostic Generation of Molecules from Shapes. - Leon Hetzel; Johanna Sommer; Bastian Rieck; Fabian J. Theis; Stephan Günnemann (CoRR, 2023)

Generative Diffusion for 3D Turbulent Flows. - Marten Lienen; Jan Hansen-Palmus; David Lüdke; Stephan Günnemann (CoRR, 2023)

Adversarial Training for Graph Neural Networks. - Lukas Gosch; Simon Geisler; Daniel Sturm 002; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (CoRR, 2023)

The power of motifs as inductive bias for learning molecular distributions. - Johanna Sommer; Leon Hetzel; David Lüdke; Fabian J. Theis; Stephan Günnemann (CoRR, 2023)

Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. - Jianxiang Feng; Matan Atad; Ismael Rodríguez; Maximilian Durner; Stephan Günnemann; Rudolph Triebel (CoRR, 2023)

AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS). - Armin Moin; Atta Badii; Stephan Günnemann; Moharram Challenger (CoRR, 2023)

Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness. - Francesco Campi; Lukas Gosch; Tom Wollschläger; Yan Scholten; Stephan Günnemann (CoRR, 2023)

Assessing Robustness via Score-Based Adversarial Image Generation. - Marcel Kollovieh; Lukas Gosch; Yan Scholten; Marten Lienen; Stephan Günnemann (CoRR, 2023)

On the Adversarial Robustness of Graph Contrastive Learning Methods. - Filippo Guerranti; Zinuo Yi; Anna Starovoit; Rafiq Kamel; Simon Geisler; Stephan Günnemann (CoRR, 2023)

Transition Path Sampling with Boltzmann Generator-based MCMC Moves. - Michael Plainer; Hannes Stärk; Charlotte Bunne; Stephan Günnemann (CoRR, 2023)

Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks. - Ege Erdogan; Simon Geisler; Stephan Günnemann (CoRR, 2023)

Graph Data Augmentation for Graph Machine Learning: A Survey. - Tong Zhao 003; Wei Jin 009; Yozen Liu; Yingheng Wang; Gang Liu 025; Stephan Günnemann; Neil Shah; Meng Jiang 001 (IEEE Data Eng. Bull., 2023)

Generalized density attractor clustering for incomplete data. - Richard Leibrandt; Stephan Günnemann (Data Min. Knowl. Discov., 2023)

Algorithm 1038: KCC: A MATLAB Package for k-Means-based Consensus Clustering. - Hao Lin 002; Hongfu Liu 001; Junjie Wu 002; Hong Li; Stephan Günnemann (ACM Trans. Math. Softw., 2023)

Understanding ReLU Network Robustness Through Test Set Certification Performance. - Nicola Franco; Jeanette Miriam Lorenz; Karsten Roscher; Stephan Günnemann (CVPR Workshops, 2024)

Towards Engineered Safe AI with Modular Concept Models. - Lena Heidemann; Iwo Kurzidem; Maureen Monnet; Karsten Roscher; Stephan Günnemann (CVPR Workshops, 2024)

From Zero to Turbulence: Generative Modeling for 3D Flow Simulation. - Marten Lienen; David Lüdke; Jan Hansen-Palmus; Stephan Günnemann (ICLR, 2024)

Uncertainty for Active Learning on Graphs. - Dominik Fuchsgruber; Tom Wollschläger; Bertrand Charpentier; Antonio Oroz; Stephan Günnemann (ICML, 2024)

Expressivity and Generalization: Fragment-Biases for Molecular GNNs. - Tom Wollschläger; Niklas Kemper; Leon Hetzel; Johanna Sommer; Stephan Günnemann (ICML, 2024)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (IJCAI, 2024)

Deep Sensor Fusion with Constraint Safety Bounds for High Precision Localization. - Sebastian Schmidt 006; Ludwig Stumpp; Diego Valverde; Stephan Günnemann (IROS, 2024)

Unified Guidance for Geometry-Conditioned Molecular Generation. - Sirine Ayadi; Leon Hetzel; Johanna Sommer; Fabian J. Theis; Stephan Günnemann (NeurIPS, 2024)

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. - Rayen Dhahri; Alexander Immer; Bertrand Charpentier; Stephan Günnemann; Vincent Fortuin (NeurIPS, 2024)

Energy-based Epistemic Uncertainty for Graph Neural Networks. - Dominik Fuchsgruber; Tom Wollschläger; Stephan Günnemann (NeurIPS, 2024)

Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations. - Nicholas Gao; Stephan Günnemann (NeurIPS, 2024)

Spatio-Spectral Graph Neural Networks. - Simon Geisler; Arthur Kosmala; Daniel Herbst; Stephan Günnemann (NeurIPS, 2024)

Expected Probabilistic Hierarchies. - Marcel Kollovieh; Bertrand Charpentier; Daniel Zügner; Stephan Günnemann (NeurIPS, 2024)

Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification. - Jan Schuchardt; Mihail Stoian; Arthur Kosmala; Stephan Günnemann (NeurIPS, 2024)

Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. - Leo Schwinn; David Dobre; Sophie Xhonneux; Gauthier Gidel; Stephan Günnemann (NeurIPS, 2024)

Efficient Adversarial Training in LLMs with Continuous Attacks. - Sophie Xhonneux; Alessandro Sordoni; Stephan Günnemann; Gauthier Gidel; Leo Schwinn (NeurIPS, 2024)

Certifiably Robust Encoding Schemes. - Aman Saxena; Tom Wollschläger; Nicola Franco; Jeanette Miriam Lorenz; Stephan Günnemann (QCE, 2024)

Discrete Randomized Smoothing Meets Quantum Computing. - Tom Wollschläger; Aman Saxena; Nicola Franco; Jeanette Miriam Lorenz; Stephan Günnemann (QCE, 2024)

Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing. - Nicola Franco; Jakob Spiegelberg; Jeanette Miriam Lorenz; Stephan Günnemann (UAI, 2024)

Attacking Large Language Models with Projected Gradient Descent. - Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Johannes Gasteiger; Stephan Günnemann (CoRR, 2024)

Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy. - Jan Schuchardt; Mihail Stoian; Arthur Kosmala; Stephan Günnemann (CoRR, 2024)

On Representing Electronic Wave Functions with Sign Equivariant Neural Networks. - Nicholas Gao; Stephan Günnemann (CoRR, 2024)

Structurally Prune Anything: Any Architecture, Any Framework, Any Time. - Xun Wang; John Rachwan; Stephan Günnemann; Bertrand Charpentier (CoRR, 2024)

Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes. - Poulami Sinhamahapatra; Suprosanna Shit; Anjany Sekuboyina; Malek El Husseini; David Schinz; Nicolas Lenhart; Bjoern H. Menze; Jan Kirschke; Karsten Roscher; Stephan Günnemann (CoRR, 2024)

Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes. - Poulami Sinhamahapatra; Franziska Schwaiger; Shirsha Bose; Huiyu Wang; Karsten Roscher; Stephan Günnemann (CoRR, 2024)

Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. - Leon Götz; Marcel Kollovieh; Stephan Günnemann; Leo Schwinn (CoRR, 2024)

Explainable Graph Neural Networks Under Fire. - Zhong Li 002; Simon Geisler; Yuhang Wang; Stephan Günnemann; Matthijs van Leeuwen (CoRR, 2024)

Unfolding Time: Generative Modeling for Turbulent Flows in 4D. - Abdullah Saydemir; Marten Lienen; Stephan Günnemann (CoRR, 2024)

Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE. - Florence Regol; Joud Chataoui; Bertrand Charpentier; Mark Coates; Pablo Piantanida; Stephan Günnemann (CoRR, 2024)

Relaxing Graph Transformers for Adversarial Attacks. - Philipp Foth; Lukas Gosch; Simon Geisler; Leo Schwinn; Stephan Günnemann (CoRR, 2024)

Extracting Unlearned Information from LLMs with Activation Steering. - Atakan Seyitoglu; Aleksei Kuvshinov; Leo Schwinn; Stephan Günnemann (CoRR, 2024)

OneProt: Towards Multi-Modal Protein Foundation Models. - Klemens Flöge; Srisruthi Udayakumar; Johanna Sommer; Marie Piraud; Stefan Kesselheim; Vincent Fortuin; Stephan Günnemann; Karel J. van der Weg; Holger Gohlke; Alina Bazarova; Erinc Merdivan (CoRR, 2024)

Assessing Robustness via Score-Based Adversarial Image Generation. - Marcel Kollovieh; Lukas Gosch; Marten Lienen; Yan Scholten; Leo Schwinn; Stephan Günnemann (Trans. Mach. Learn. Res., 2024)

Machine Learning-Enabled Software and System Architecture Frameworks. - Armin Moin; Atta Badii; Stephan Günnemann; Moharram Challenger (Zenodo, 2024)

Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems. - Sebastian Schmidt 006; Lukas Stappen; Leo Schwinn; Stephan Günnemann (IEEE Robotics Autom. Lett., 2024)

Joint Out-of-Distribution Filtering and Data Discovery Active Learning. - Sebastian Schmidt 006; Leonard Schenk; Leo Schwinn; Stephan Günnemann (CVPR, 2025)

Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance. - Dominik Fuchsgruber; Tim Postuvan; Stephan Günnemann; Simon Geisler (ICLR, 2025)

Learning Equivariant Non-Local Electron Density Functionals. - Nicholas Gao; Eike Eberhard; Stephan Günnemann (ICLR, 2025)

MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds. - Leon Hetzel; Johanna Sommer; Bastian Rieck; Fabian J. Theis; Stephan Günnemann (ICLR, 2025)

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space. - Mohamed Amine Ketata; Nicholas Gao; Johanna Sommer; Tom Wollschläger; Stephan Günnemann (ICLR, 2025)

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting. - Marcel Kollovieh; Marten Lienen; David Lüdke; Leo Schwinn; Stephan Günnemann (ICLR, 2025)

Unlocking Point Processes through Point Set Diffusion. - David Lüdke; Enric Rabasseda Raventós; Marcel Kollovieh; Stephan Günnemann (ICLR, 2025)

Exact Certification of (Graph) Neural Networks Against Label Poisoning. - Mahalakshmi Sabanayagam; Lukas Gosch; Stephan Günnemann; Debarghya Ghoshdastidar (ICLR, 2025)

Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning. - Yan Scholten; Stephan Günnemann (ICLR, 2025)

A Probabilistic Perspective on Unlearning and Alignment for Large Language Models. - Yan Scholten; Stephan Günnemann; Leo Schwinn (ICLR, 2025)

Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models. - Poulami Sinhamahapatra; Shirsha Bose; Karsten Roscher; Stephan Günnemann (NLDL, 2025)

Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes. - Poulami Sinhamahapatra; Franziska Schwaiger; Shirsha Bose; Huiyu Wang; Karsten Roscher; Stephan Günnemann (WACV, 2025)

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. - Xuan Zhang; Limei Wang; Jacob Helwig; Youzhi Luo; Cong Fu 003; Yaochen Xie; Meng Liu 015; Yuchao Lin; Zhao Xu 005; Keqiang Yan; Keir Adams; Maurice Weiler; et al. (Found. Trends Mach. Learn., 2025)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. - Lukas Gosch; Mahalakshmi Sabanayagam; Debarghya Ghoshdastidar; Stephan Günnemann (Trans. Mach. Learn. Res., 2025)

A Unified Approach Towards Active Learning and Out-of-Distribution Detection. - Sebastian Schmidt 006; Leonard Schenk; Leo Schwinn; Stephan Günnemann (Trans. Mach. Learn. Res., 2025)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting. - Jan Schuchardt; Mina Dalirrooyfard; Jed Guzelkabaagac; Anderson Schneider; Yuriy Nevmyvaka; Stephan Günnemann (CoRR, 2025)

Rethinking Link Prediction for Directed Graphs. - Mingguo He; Yuhe Guo; Yanping Zheng; Zhewei Wei; Stephan Günnemann; Xiaokui Xiao (CoRR, 2025)

Generative Modeling with Bayesian Sample Inference. - Marten Lienen; Marcel Kollovieh; Stephan Günnemann (CoRR, 2025)

Fast Proxies for LLM Robustness Evaluation. - Tim Beyer; Jan Schuchardt; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives. - Leo Schwinn; Yan Scholten; Tom Wollschläger; Sophie Xhonneux; Stephen Casper; Stephan Günnemann; Gauthier Gidel (CoRR, 2025)

REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective. - Simon Geisler; Tom Wollschläger; M. H. I. Abdalla; Vincent Cohen-Addad; Johannes Gasteiger; Stephan Günnemann (CoRR, 2025)

The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence. - Tom Wollschläger; Jannes Elstner; Simon Geisler; Vincent Cohen-Addad; Stephan Günnemann; Johannes Gasteiger (CoRR, 2025)

LLM-Safety Evaluations Lack Robustness. - Tim Beyer; Sophie Xhonneux; Simon Geisler; Gauthier Gidel; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

LipShiFT: A Certifiably Robust Shift-based Vision Transformer. - Rohan Menon; Nicola Franco; Stephan Günnemann (CoRR, 2025)

Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation. - Sebastian Schmidt 006; Julius Körner; Dominik Fuchsgruber; Stefano Gasperini; Federico Tombari; Stephan Günnemann (CoRR, 2025)

Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems. - Michael Scherbela; Nicholas Gao; Philipp Grohs; Stephan Günnemann (CoRR, 2025)

Targeted AMP generation through controlled diffusion with efficient embeddings. - Diogo Soares; Leon Hetzel; Paulina Szymczak; Fabian J. Theis; Stephan Günnemann; Ewa Szczurek (CoRR, 2025)

Byte Pair Encoding for Efficient Time Series Forecasting. - Leon Götz; Marcel Kollovieh; Stephan Günnemann; Leo Schwinn (CoRR, 2025)

Joint Relational Database Generation via Graph-Conditional Diffusion Models. - Mohamed Amine Ketata; David Lüdke; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory. - Dominik Fuchsgruber; Tom Wollschläger; Johannes Bordne; Stephan Günnemann (CoRR, 2025)

Evaluating the transferability of adversarial robustness to target domains. - Anna-Kathrin Kopetzki; Aleksandar Bojchevski; Stephan Günnemann (Knowl. Inf. Syst., 2025)

UnHiPPO: Uncertainty-aware Initialization for State Space Models. - Marten Lienen; Abdullah Saydemir; Stephan Günnemann (CoRR, 2025)

Effective Data Pruning through Score Extrapolation. - Sebastian Schmidt 006; Prasanga Dhungel; Christoffer Löffler; Björn Nieth; Stephan Günnemann; Leo Schwinn (CoRR, 2025)

Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models. - Michael Plainer; Hao Wu 035; Leon Klein; Stephan Günnemann; Frank Noé (CoRR, 2025)

Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs. - Yan Scholten; Sophie Xhonneux; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking. - Tim Beyer; Yan Scholten; Leo Schwinn; Stephan Günnemann (CoRR, 2025)

Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation. - Alessandro Palma; Sergei Rybakov; Leon Hetzel; Stephan Günnemann; Fabian J. Theis (CoRR, 2025)

SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation. - Rafiq Kamel; Filippo Guerranti; Simon Geisler; Stephan Günnemann (CoRR, 2025)