Stephan Günnemann

发表

SMVC: semi-supervised multi-view clustering in subspace projections

Thomas Seidl, Stephan Günnemann, Ines Färber, 2014, KDD.

Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data

Ira Assent, Ralph Krieger, Emmanuel Müller, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Directional Message Passing for Molecular Graphs pdf

Stephan Günnemann, Johannes Klicpera, Janek Groß, 2020, ICLR.

Adversarial Attacks on Graph Neural Networks

Stephan Günnemann, Daniel Zügner, Amir Akbarnejad, 2019, GI-Jahrestagung.

Adversarial Attacks on Node Embeddings via Graph Poisoning

Stephan Günnemann, Aleksandar Bojchevski, Aleksandar Bojchevski, 2018, ICML.

MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions

Thomas Seidl, Stephan Günnemann, Ines Färber, 2010, 2010 IEEE International Conference on Data Mining Workshops.

Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training pdf

Stephan Günnemann, Anna-Kathrin Kopetzki, 2020, ArXiv.

Introduction to Tensor Decompositions and their Applications in Machine Learning pdf

Stephan Günnemann, Stephan Rabanser, Oleksandr Shchur, 2017, ArXiv.

Scalable density-based subspace clustering

Ira Assent, Emmanuel Müller, Thomas Seidl, 2011, CIKM '11.

Deep Representation Learning and Clustering of Traffic Scenarios pdf

Stephan Günnemann, Nick Harmening, Marin Bilos, 2020, ArXiv.

Extracting Taxonomies from Bipartite Graphs

Christos Faloutsos, Stephan Günnemann, Tobias Kötter, 2015, WWW.

Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking pdf

Stephan Günnemann, Aleksandar Bojchevski, Aleksandar Bojchevski, 2017, ArXiv.

MLearn: A Declarative Machine Learning Language for Database Systems

Stephan Günnemann, Alfons Kemper, Maximilian E. Schüle, 2019, DEEM@SIGMOD.

ThingML+: Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning

Stephan Günnemann, Armin Moin, Stephan Rössler, 2020, MoDELS.

Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection

Stephan Günnemann, Felippe Schmoeller Roza, Maximilian Henne, 2020, ECCV Workshops.

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation

Christos Faloutsos, Stephan Günnemann, Dhivya Eswaran, 2017, SDM.

Uncertainty on Asynchronous Time Event Prediction

Stephan Günnemann, Marin Bilos, Bertrand Charpentier, 2019, NeurIPS.

KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks pdf

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2014, ArXiv.

Efficient Robustness Certificates for Graph Neural Networks via Sparsity-Aware Randomized Smoothing

Stephan Günnemann, Johannes Klicpera, Aleksandar Bojchevski, 2020, ICML 2020.

SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2017, EDBT.

Providing guidance on efficient flash flood documentation: an application based approach

Stephan Günnemann, Maria Katharina Kaiser, Markus Disse, 2020 .

Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types

Thomas Seidl, Stephan Günnemann, Brigitte Boden, 2012, SSDBM.

Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2017, Proc. VLDB Endow..

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts pdf

Stephan Günnemann, Daniel Zügner, Bertrand Charpentier, 2020, NeurIPS.

Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data

Emmanuel Müller, Thomas Seidl, Stephan Günnemann, 2010, 2012 IEEE 28th International Conference on Data Engineering.

In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2019, BTW.

Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2017, Datenbank-Spektrum.

Intensity-Free Learning of Temporal Point Processes pdf

Stephan Günnemann, Oleksandr Shchur, Marin Bilos, 2020, ICLR.

Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation

Stephan Günnemann, Richard Leibrandt, Stephan Günnemann, 2018, SDM.

Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference

Stephan Günnemann, Sebastian Steinhorst, Artur Mrowca, 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

An Evaluation Framework for Temporal Subspace Clustering Approaches

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Reliable Graph Neural Networks via Robust Aggregation pdf

Stephan Günnemann, Daniel Zügner, Simon Geisler, 2020, NeurIPS.

Finding density-based subspace clusters in graphs with feature vectors

Thomas Seidl, Stephan Günnemann, Brigitte Boden, 2012, Data Mining and Knowledge Discovery.

Preferential Attachment in Graphs with Affinities

Alexander J. Smola, Jay Lee, Stephan Günnemann, 2015, AISTATS.

Dual-Primal Graph Convolutional Networks pdf

Stephan Günnemann, Michael M. Bronstein, Aleksandar Bojchevski, 2018, ArXiv.

Less is More: Non-Redundant Subspace Clustering

Ira Assent, Ralph Krieger, Emmanuel Müller, 2010 .

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? pdf

Stephan Günnemann, Daniel Zügner, Anna-Kathrin Kopetzki, 2020, ArXiv.

Subspace Clustering for Uncertain Data

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2010, SDM.

The Power of SQL Lambda Functions

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2019, EDBT.

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting

Stephan Günnemann, Syama Sundar Rangapuram, Emmanuel de Bézenac, 2020, NeurIPS.

Tracing clusters in evolving graphs with node attributes

Thomas Seidl, Stephan Günnemann, Brigitte Boden, 2012, CIKM '12.

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Stephan Günnemann, Aleksandar Bojchevski, Aleksandar Bojchevski, 2017, ICLR.

Diffusion Improves Graph Learning

Stephan Günnemann, Johannes Klicpera, Stefan Weißenberger, 2019, NeurIPS.

Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2010, 2010 IEEE International Conference on Data Mining Workshops.

Stochastic subspace search for top-k multi-view clustering

Geng Li, Mohammed J. Zaki, Stephan Günnemann, 2013, MultiClust@KDD.

Com2: Fast Automatic Discovery of Temporal ('Comet') Communities

Ananthram Swami, Danai Koutra, Christos Faloutsos, 2014, PAKDD.

From things' modeling language (ThingML) to things' machine learning (ThingML2)

Stephan Günnemann, Armin Moin, Stephan Rössler, 2020, MODELS Companion.

Equivariant Normalizing Flows for Point Processes and Sets pdf

Stephan Günnemann, Marin Bilos, Stephan Günnemann, 2020, ArXiv.

Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations

Stephan Günnemann, Daniel Zügner, Stephan Günnemann, 2020, KDD.

DensEst: Density Estimation for Data Mining in High Dimensional Spaces

Ira Assent, Ralph Krieger, Emmanuel Müller, 2009, SDM.

Linearized and Single-Pass Belief Propagation

Danai Koutra, Christos Faloutsos, Stephan Günnemann, 2014, Proc. VLDB Endow..

Oktoberfest Food Dataset pdf

Stephan Günnemann, Daniel Zügner, Peter Vogel, 2019, ArXiv.

GraphGAN: Generating Graphs via Random Walks

Stephan Günnemann, Daniel Zügner, Aleksandar Bojchevski, 2018 .

Machine Learning Meets Databases

Stephan Günnemann, Stephan Günnemann, 2017, Datenbank-Spektrum.

Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques

Pauli Miettinen, Stephan Günnemann, Saskia Metzler, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Robust multivariate autoregression for anomaly detection in dynamic product ratings

Christos Faloutsos, Nikou Günnemann, Stephan Günnemann, 2014, WWW.

Certifiable Robustness to Graph Perturbations

Stephan Günnemann, Aleksandar Bojchevski, Aleksandar Bojchevski, 2019, NeurIPS.

Pattern detector: fast detection of suspicious stream patterns for immediate reaction

Ira Assent, Thomas Seidl, Stephan Günnemann, 2010, EDBT '10.

Certifiable Robustness and Robust Training for Graph Convolutional Networks

Stephan Günnemann, Daniel Zügner, Stephan Günnemann, 2019, KDD.

ZooBP: Belief Propagation for Heterogeneous Networks

Christos Faloutsos, Stephan Günnemann, Mohit Kumar, 2017, Proc. VLDB Endow..

MultiClust special issue on discovering, summarizing and using multiple clusterings

Ira Assent, Emmanuel Müller, Thomas Seidl, 2014, Machine Learning.

Assessing the Significance of Data Mining Results on Graphs with Feature Vectors

Martin Ester, Stephan Günnemann, Mohsen Jamali, 2012, 2012 IEEE 12th International Conference on Data Mining.

Scene Graph Reasoning for Visual Question Answering pdf

Stephan Günnemann, Volker Tresp, Hang Li, 2020, ArXiv.

Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data

Emmanuel Müller, Thomas Seidl, Stephan Günnemann, 2012, ICDE.

Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data

Thomas Seidl, Stephan Günnemann, Ines Färber, 2012, KDD.

Pitfalls of Graph Neural Network Evaluation pdf

Stephan Günnemann, Aleksandar Bojchevski, Oleksandr Shchur, 2018, ArXiv.

Subgraph Mining on Directed and Weighted Graphs

Thomas Seidl, Stephan Günnemann, Stephan Günnemann, 2010, PAKDD.

High-Dimensional Indexing for Multimedia Features

Ira Assent, Thomas Seidl, Stephan Günnemann, 2009, BTW.

Tracing Evolving Subspace Clusters in Temporal Climate Data

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2011, Data Mining and Knowledge Discovery.

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

Stephan Günnemann, Zachary C. Lipton, Stephan Rabanser, 2018, NeurIPS.

Subspace clustering for complex data

Stephan Günnemann, Stephan Günnemann, 2012, BTW.

Overlapping Community Detection with Graph Neural Networks pdf

Stephan Günnemann, Oleksandr Shchur, Stephan Günnemann, 2018, ArXiv.

Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2017, BTW.

Automatic Taxonomy Extraction from Bipartite Graphs

Christos Faloutsos, Stephan Günnemann, Tobias Kötter, 2015, 2015 IEEE International Conference on Data Mining.

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings

Stephan Günnemann, Aleksandar Bojchevski, Yves Matkovic, 2017, KDD.

Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2012, PAKDD.

Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping

Stephan Günnemann, Artur Mrowca, Barbara Moser, 2018, ECML/PKDD.

OpenSubspace: An Open Source Framework for Evaluation and Exploration of Subspace Clustering Algorithms in WEKA

Ira Assent, Emmanuel Müller, Stephan Günnemann, 2009 .

MC-Tree: Improving Bayesian Anytime Classification

Thomas Seidl, Stephan Günnemann, Philipp Kranen, 2010, SSDBM.

Flexible Fault Tolerant Subspace Clustering for Data with Missing Values

Emmanuel Müller, Thomas Seidl, Stephan Günnemann, 2011, 2011 IEEE 11th International Conference on Data Mining.

Detecting anomalies in dynamic rating data

Christos Faloutsos, Nikou Günnemann, Stephan Günnemann, 2014, KDD 2014.

DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors

Thomas Seidl, Stephan Günnemann, Brigitte Boden, 2011, ECML/PKDD.

Fault-Tolerant Concept Detection in Information Networks

Christos Faloutsos, Stephan Günnemann, Tobias Kötter, 2014, PAKDD.

Adversarial Attacks on Neural Networks for Graph Data

Stephan Günnemann, Daniel Zügner, Amir Akbarnejad, 2018, KDD.

Beyond Blocks: Hyperbolic Community Detection

Christos Faloutsos, Gonzalo Mateos, Stephan Günnemann, 2014, ECML/PKDD.

Mining Contrasting Quasi-Clique Patterns pdf

Stephan Günnemann, Roberto Alonso, Stephan Günnemann, 2018, ArXiv.

GhostLink: Latent Network Inference for Influence-aware Recommendation

Subhabrata Mukherjee, Stephan Günnemann, Stephan Günnemann, 2019, WWW.

MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels

Holger Hoffmann, Thomas Seidl, Stephan Günnemann, 2017, Knowledge and Information Systems.

An extension of the PMML standard to subspace clustering models

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2011, PMML '11.

Nesting the earth mover's distance for effective cluster tracing

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2013, SSDBM.

An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors

Stephan Günnemann, Matthias Schubert, Jan Stutzki, 2018, SDM.

External evaluation measures for subspace clustering

Ira Assent, Emmanuel Müller, Thomas Seidl, 2011, CIKM '11.

Detection of orthogonal concepts in subspaces of high dimensional data

Emmanuel Müller, Thomas Seidl, Stephan Günnemann, 2009, CIKM.

Stability and dynamics of communities on online question-answer sites

Pauli Miettinen, Stephan Günnemann, Saskia Metzler, 2019, Soc. Networks.

Continual Learning with Bayesian Neural Networks for Non-Stationary Data

Botond Cseke, Patrick van der Smagt, Stephan Günnemann, 2020, ICLR.

Linearized and Turbo Belief Propagation pdf

Danai Koutra, Christos Faloutsos, Stephan Günnemann, 2014, ArXiv.

Group Centrality Maximization for Large-scale Graphs pdf

Stephan Günnemann, Henning Meyerhenke, Daniel Zügner, 2019, ALENEX.

Finding contexts of social influence in online social networks

Bo Hu, Martin Ester, Stephan Günnemann, 2013, SNAKDD '13.

Mixed Membership Subspace Clustering

Christos Faloutsos, Stephan Günnemann, C. Faloutsos, 2013, 2013 IEEE 13th International Conference on Data Mining.

Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2011, EDBT/ICDT '11.

Effective and Robust Mining of Temporal Subspace Clusters

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2012, 2012 IEEE 12th International Conference on Data Mining.

Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics

Stephan Günnemann, Bernard Bäker, Peter Wolf, 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Adversarial Attacks on Classification Models for Graphs

Stephan Günnemann, Daniel Zügner, Amir Akbarnejad, 2018 .

NetGAN: Generating Graphs via Random Walks pdf

Stephan Günnemann, Daniel Zügner, Aleksandar Bojchevski, 2018, ICML.

EdgeCentric: Anomaly Detection in Edge-Attributed Networks

Christos Faloutsos, Leman Akoglu, Stephan Günnemann, 2015, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank pdf

Stephan Günnemann, Johannes Klicpera, Aleksandar Bojchevski, 2018, ArXiv.

Efficient Processing of Multiple DTW Queries in Time Series Databases

Ira Assent, Thomas Seidl, Stephan Günnemann, 2011, SSDBM.

Evaluating Clustering in Subspace Projections of High Dimensional Data

Ira Assent, Emmanuel Müller, Thomas Seidl, 2009, Proc. VLDB Endow..

CoDA: Interactive Cluster Based Concept Discovery

Thomas Seidl, Stephan Günnemann, Ines Färber, 2010, Proc. VLDB Endow..

Predict then Propagate: Combining neural networks with personalized pagerank for classification on graphs

Stephan Günnemann, Johannes Klicpera, Aleksandar Bojchevski, 2018, ICLR 2018.

Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms

Thomas Seidl, Stephan Günnemann, Ines Färber, 2010, 2010 IEEE International Conference on Data Mining.

ML2SQL - Compiling a Declarative Machine Learning Language to SQL and Python

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2019, EDBT.

A Subspace Clustering Extension for the KNIME Data Mining Framework

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution

Christos Faloutsos, Nikou Günnemann, Stephan Günnemann, 2014, KDD.

Spectral Subspace Clustering for Graphs with Feature Vectors

Thomas Seidl, Stephan Günnemann, Ines Färber, 2013, 2013 IEEE 13th International Conference on Data Mining.

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More pdf

Stephan Günnemann, Johannes Klicpera, Aleksandar Bojchevski, 2020, ICML.

Effiziente Integration von Data- und Graph-Mining-Algorithmen in relationale Datenbanksysteme

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2015, LWA.

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure

Stephan Günnemann, Aleksandar Bojchevski, Aleksandar Bojchevski, 2018, AAAI.

Adversarial Attacks on Neural Networks for Graph Data

Stephan Günnemann, Daniel Zügner, Amir Akbarnejad, 2018, KDD.

Anomaly Detection in Car-Booking Graphs

Stephan Günnemann, Aleksandar Bojchevski, Yusuf Saber, 2018, 2018 IEEE International Conference on Data Mining Workshops (ICDMW).

GAMer: a synthesis of subspace clustering and dense subgraph mining

Thomas Seidl, Stephan Günnemann, Ines Färber, 2013, Knowledge and Information Systems.

Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs pdf

Stephan Günnemann, Zhen Han, Volker Tresp, 2020, ArXiv.

Tracing Evolving Clusters by Subspace and Value Similarity

Thomas Seidl, Stephan Günnemann, Hardy Kremer, 2011, PAKDD.

Discovery of “comet” communities in temporal and labeled graphs Com$$^2$$2

Ananthram Swami, Danai Koutra, Christos Faloutsos, 2015, Knowledge and Information Systems.

Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors

Thomas Seidl, Stephan Günnemann, Ines Färber, 2013, PAKDD.

Adversarial Attacks on Graph Neural Networks via Meta Learning pdf

Stephan Günnemann, Daniel Zügner, Stephan Günnemann, 2019, ICLR.

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection pdf

Christos Faloutsos, Leman Akoglu, Stephan Günnemann, 2015, SDM.

Proceedings of OSDM 2009

Ira Assent, Emmanuel Müller, Thomas Seidl, 2009 .

Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs

Stephan Günnemann, Zhen Han, Yunpu Ma, 2020, AKBC.

Continuous Experience-aware Language Model

Gerhard Weikum, Subhabrata Mukherjee, Stephan Günnemann, 2016, KDD.

Adversarial Attacks on Node Embeddings pdf

Stephan Günnemann, Aleksandar Bojchevski, Aleksandar Bojchevski, 2018, ICML 2019.

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Stephan Günnemann, Johannes Klicpera, Aleksandar Bojchevski, 2018, ICLR.

Multi-Source Neural Variational Inference pdf

Patrick van der Smagt, Stephan Günnemann, Richard Kurle, 2018, AAAI.

Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs

Stephan Günnemann, Michael E. Houle, Lorenzo von Ritter, 2018, SISAP.

Multi-view clustering using mixture models in subspace projections

Thomas Seidl, Stephan Günnemann, Ines Färber, 2012, KDD.

A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases

Ira Assent, Emmanuel Müller, Thomas Seidl, 2011, BTW.

RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs

Holger Hoffmann, Thomas Seidl, Stephan Günnemann, 2013, SSDBM.

Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data

Andreas Züfle, Stephan Günnemann, Ruixin Yang, 2017, SSTD.

Directional Message Passing on Molecular Graphs via Synthetic Coordinates pdf

Stephan Günnemann, Chandan Yeshwanth, Johannes Klicpera, 2021, ArXiv.

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph) pdf

Stephan Günnemann, Bjoern H. Menze, Bjoern H Menze, 2021, NeurIPS Datasets and Benchmarks.

On Out-of-distribution Detection with Energy-based Models pdf

Stephan Günnemann, Daniel Zügner, Sven Elflein, 2021, ArXiv.

Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications pdf

Stephan Günnemann, Atta Badii, Armin Moin, 2021, ArXiv.

Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering pdf

Stephan Günnemann, Volker Tresp, Marcel Hildebrandt, 2021, SEMWEB.

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks

Stephan Günnemann, Aleksandar Bojchevski, Johannes Klicpera, 2023, ICLR.

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions pdf

Stephan Günnemann, Nicholas Gao, 2021, ArXiv.

In-Database Machine Learning with SQL on GPUs

Stephan Günnemann, Alfons Kemper, Thomas Neumann, 2021, SSDBM.

Scalable Normalizing Flows for Permutation Invariant Densities

Stephan Günnemann, Marin Bilos, 2021, ICML.

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services pdf

Stephan Günnemann, Atta Badii, Armin Moin, 2021, ArXiv.

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More pdf

Stephan Günnemann, Johannes Klicpera, Marten Lienen, 2021, ICML.

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Stephan Günnemann, Daniel Zügner, Anna-Kathrin Kopetzki, 2020, ICML.

OODformer: Out-Of-Distribution Detection Transformer pdf

Stephan Günnemann, Volker Tresp, Rajat Koner, 2021, BMVC.

A Model-Driven Engineering Approach to Machine Learning and Software Modeling pdf

Stephan Günnemann, Atta Badii, Armin Moin, 2021, ArXiv.

A Study of Joint Graph Inference and Forecasting pdf

Stephan Günnemann, Daniel Zügner, Tim Januschowski, 2021, ArXiv.

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Stephan Günnemann, Artur Mrowca, Florian Gyrock, 2022, Data Min. Knowl. Discov..

Mining communities and their descriptions on attributed graphs: a survey

Martin Atzmüller, Stephan Günnemann, Albrecht Zimmermann, 2021, Data Mining and Knowledge Discovery.

Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions

Stephan Günnemann, Aleksandar Bojchevski, Yihan Wu, 2021, AISTATS.

Intriguing Properties of Input-dependent Randomized Smoothing pdf

Stephan Günnemann, Aleksei Kuvshinov, Peter Súkeník, 2021, ArXiv.

Neural Temporal Point Processes: A Review pdf

Stephan Günnemann, Tim Januschowski, Ali Caner Türkmen, 2021, IJCAI.

Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments

Stephan Günnemann, Felippe Schmoeller Roza, Karsten Roscher, 2021, AISafety@IJCAI.

Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training

Stephan Günnemann, Anna-Kathrin Kopetzki, 2021, Mach. Learn..