Stephan Günnemann

Orcid: 0000-0001-7772-5059

Affiliations:
  • Technical University of Munich, Germany
  • Carnegie Mellon University, Pittsburgh, USA (former)


According to our database1, Stephan Günnemann authored at least 281 papers between 2009 and 2024.

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Bibliography

2024
Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems.
IEEE Robotics Autom. Lett., October, 2024

Unlocking Point Processes through Point Set Diffusion.
CoRR, 2024

Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance.
CoRR, 2024

Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning.
CoRR, 2024

Learning Equivariant Non-Local Electron Density Functionals.
CoRR, 2024

A Probabilistic Perspective on Unlearning and Alignment for Large Language Models.
CoRR, 2024

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting.
CoRR, 2024

Certifiably Robust Encoding Schemes.
CoRR, 2024

Discrete Randomized Smoothing Meets Quantum Computing.
CoRR, 2024

Relaxing Graph Transformers for Adversarial Attacks.
CoRR, 2024

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks.
CoRR, 2024

Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE.
CoRR, 2024

Unfolding Time: Generative Modeling for Turbulent Flows in 4D.
CoRR, 2024

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space.
CoRR, 2024

Explainable Graph Neural Networks Under Fire.
CoRR, 2024

Energy-based Epistemic Uncertainty for Graph Neural Networks.
CoRR, 2024

Spatio-Spectral Graph Neural Networks.
CoRR, 2024

Efficient Time Series Processing for Transformers and State-Space Models through Token Merging.
CoRR, 2024

Efficient Adversarial Training in LLMs with Continuous Attacks.
CoRR, 2024

Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations.
CoRR, 2024

A Unified Approach Towards Active Learning and Out-of-Distribution Detection.
CoRR, 2024

Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes.
CoRR, 2024

Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes.
CoRR, 2024

Structurally Prune Anything: Any Architecture, Any Framework, Any Time.
CoRR, 2024

On Representing Electronic Wave Functions with Sign Equivariant Neural Networks.
CoRR, 2024

Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy.
CoRR, 2024

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood.
CoRR, 2024

Attacking Large Language Models with Projected Gradient Descent.
CoRR, 2024

Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space.
CoRR, 2024

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract).
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Uncertainty for Active Learning on Graphs.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Zero to Turbulence: Generative Modeling for 3D Flow Simulation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Towards Engineered Safe AI with Modular Concept Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Understanding ReLU Network Robustness Through Test Set Certification Performance.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Algorithm 1038: KCC: A MATLAB Package for <i>k</i>-Means-based Consensus Clustering.
ACM Trans. Math. Softw., December, 2023

Generalized density attractor clustering for incomplete data.
Data Min. Knowl. Discov., March, 2023

Graph Data Augmentation for Graph Machine Learning: A Survey.
IEEE Data Eng. Bull., 2023

Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks.
CoRR, 2023

Transition Path Sampling with Boltzmann Generator-based MCMC Moves.
CoRR, 2023

On the Adversarial Robustness of Graph Contrastive Learning Methods.
CoRR, 2023

Assessing Robustness via Score-Based Adversarial Image Generation.
CoRR, 2023

Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness.
CoRR, 2023

AI-Enabled Software and System Architecture Frameworks: Focusing on smart Cyber-Physical Systems (CPS).
CoRR, 2023

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.
CoRR, 2023

Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly.
CoRR, 2023

The power of motifs as inductive bias for learning molecular distributions.
CoRR, 2023

Adversarial Training for Graph Neural Networks.
CoRR, 2023

Generative Diffusion for 3D Turbulent Flows.
CoRR, 2023

MAGNet: Motif-Agnostic Generation of Molecules from Shapes.
CoRR, 2023

Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models.
CoRR, 2023

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection.
CoRR, 2023

Training, Architecture, and Prior for Deterministic Uncertainty Methods.
CoRR, 2023

Training Differentially Private Graph Neural Networks with Random Walk Sampling.
CoRR, 2023

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hierarchical Randomized Smoothing.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Add and Thin: Diffusion for Temporal Point Processes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Edge Directionality Improves Learning on Heterophilic Graphs.
Proceedings of the Learning on Graphs Conference, 27-30 November 2023, Virtual Event., 2023

Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework.
Proceedings of the IEEE Intelligent Vehicles Symposium, 2023

Uncertainty Estimation for Molecules: Desiderata and Methods.
Proceedings of the International Conference on Machine Learning, 2023

Ewald-based Long-Range Message Passing for Molecular Graphs.
Proceedings of the International Conference on Machine Learning, 2023

Transformers Meet Directed Graphs.
Proceedings of the International Conference on Machine Learning, 2023

Generalizing Neural Wave Functions.
Proceedings of the International Conference on Machine Learning, 2023

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion.
Proceedings of the International Conference on Machine Learning, 2023

Localized Randomized Smoothing for Collective Robustness Certification.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unveiling the sampling density in non-uniform geometric graphs.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Revisiting Robustness in Graph Machine Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Adversarial Attacks and Defenses in Large Language Models: Old and New Threats.
Proceedings of the Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 2023

Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning.
Proceedings of the Conference on Robot Learning, 2023

Enabling Machine Learning in Software Architecture Frameworks.
Proceedings of the 2nd IEEE/ACM International Conference on AI Engineering, 2023

Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss.
Proceedings of the 34th British Machine Vision Conference 2023, 2023

Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out Of Distribution.
Proceedings of the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023) co-located with the 32nd International Joint Conference on Artificial Intelligence(IJCAI2023), 2023

2022
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
Mach. Learn. Sci. Technol., December, 2022

C.DOT - Convolutional Deep Object Tracker for Augmented Reality Based Purely on Synthetic Data.
IEEE Trans. Vis. Comput. Graph., 2022

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets.
Trans. Mach. Learn. Res., 2022

A model-driven approach to machine learning and software modeling for the IoT.
Softw. Syst. Model., 2022

Robustness verification of ReLU networks via quadratic programming.
Mach. Learn., 2022

Recursive SQL and GPU-support for in-database machine learning.
Distributed Parallel Databases, 2022

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation.
Data Min. Knowl. Discov., 2022

Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132).
Dagstuhl Reports, 2022

Modeling Temporal Data as Continuous Functions with Process Diffusion.
CoRR, 2022

torchode: A Parallel ODE Solver for PyTorch.
CoRR, 2022

Irregularly-Sampled Time Series Modeling with Spline Networks.
CoRR, 2022

United States Politicians' Tone Became More Negative with 2016 Primary Campaigns.
CoRR, 2022

On the Robustness and Anomaly Detection of Sparse Neural Networks.
CoRR, 2022

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning.
CoRR, 2022

Predicting single-cell perturbation responses for unseen drugs.
CoRR, 2022

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?
CoRR, 2022

Enabling Automated Machine Learning for Model-Driven AI Engineering.
CoRR, 2022

Graph Data Augmentation for Graph Machine Learning: A Survey.
CoRR, 2022

Multi-Objective Model Selection for Time Series Forecasting.
CoRR, 2022

Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm.
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2022

Invariance-Aware Randomized Smoothing Certificates.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Are Defenses for Graph Neural Networks Robust?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MDE for machine learning-enabled software systems: a case study and comparison of MontiAnna & ML-Quadrat.
Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, 2022

A Systematic Evaluation of Node Embedding Robustness.
Proceedings of the Learning on Graphs Conference, 2022

Influence-Based Mini-Batching for Graph Neural Networks.
Proceedings of the Learning on Graphs Conference, 2022

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services.
Proceedings of the 44th IEEE/ACM International Conference on Software Engineering: Companion Proceedings, 2022

Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning.
Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

Intriguing Properties of Input-Dependent Randomized Smoothing.
Proceedings of the International Conference on Machine Learning, 2022

3D Infomax improves GNNs for Molecular Property Prediction.
Proceedings of the International Conference on Machine Learning, 2022

Winning the Lottery Ahead of Time: Efficient Early Network Pruning.
Proceedings of the International Conference on Machine Learning, 2022

End-to-End Learning of Probabilistic Hierarchies on Graphs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Differentiable DAG Sampling.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Towards Model-Driven Engineering for Quantum AI.
Proceedings of the 52. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2022, Informatik in den Naturwissenschaften, 26., 2022

Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

Supporting AI Engineering on the IoT Edge through Model-Driven TinyML.
Proceedings of the 46th IEEE Annual Computers, Software, and Applications Conferenc, 2022

Is it all a cluster game? - Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space.
Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022) co-located with the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), 2022

Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training.
Mach. Learn., 2021

Mining communities and their descriptions on attributed graphs: a survey.
Data Min. Knowl. Discov., 2021

A Study of Joint Graph Inference and Forecasting.
CoRR, 2021

On Second-order Optimization Methods for Federated Learning.
CoRR, 2021

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph).
CoRR, 2021

On Out-of-distribution Detection with Energy-based Models.
CoRR, 2021

MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence.
CoRR, 2021

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services.
CoRR, 2021

Enabling Un-/Semi-Supervised Machine Learning for MDSE of the Real-World CPS/IoT Applications.
CoRR, 2021

A Model-Driven Engineering Approach to Machine Learning and Software Modeling.
CoRR, 2021

In-Database Machine Learning with SQL on GPUs.
Proceedings of the SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management, 2021

Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering.
Proceedings of the Semantic Web - ISWC 2021 - 20th International Semantic Web Conference, 2021

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Detecting Anomalous Event Sequences with Temporal Point Processes.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Robustness of Graph Neural Networks at Scale.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Directional Message Passing on Molecular Graphs via Synthetic Coordinates.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

GemNet: Universal Directional Graph Neural Networks for Molecules.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Flows: Efficient Alternative to Neural ODEs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Temporal Point Processes: A Review.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Domain Shifts in Reinforcement Learning: Identifying Disturbances in Environments.
Proceedings of the Workshop on Artificial Intelligence Safety 2021 co-located with the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021), 2021

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Proceedings of the 38th International Conference on Machine Learning, 2021

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More.
Proceedings of the 38th International Conference on Machine Learning, 2021

Scalable Normalizing Flows for Permutation Invariant Densities.
Proceedings of the 38th International Conference on Machine Learning, 2021

Language-Agnostic Representation Learning of Source Code from Structure and Context.
Proceedings of the 9th International Conference on Learning Representations, 2021

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks.
Proceedings of the 9th International Conference on Learning Representations, 2021

OODformer: Out-Of-Distribution Detection Transformer.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021

Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns.
ACM Trans. Knowl. Discov. Data, 2020

Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules.
CoRR, 2020

Equivariant Normalizing Flows for Point Processes and Sets.
CoRR, 2020

Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training.
CoRR, 2020

Deep Representation Learning and Clustering of Traffic Scenarios.
CoRR, 2020

Scene Graph Reasoning for Visual Question Answering.
CoRR, 2020

Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs.
CoRR, 2020

Gauss Shift: Density Attractor Clustering Faster Than Mean Shift.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Fast and Flexible Temporal Point Processes with Triangular Maps.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Reliable Graph Neural Networks via Robust Aggregation.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

From things' modeling language (ThingML) to things' machine learning (ThingML2).
Proceedings of the MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems, 2020

Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Scaling Graph Neural Networks with Approximate PageRank.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More.
Proceedings of the 37th International Conference on Machine Learning, 2020

Intensity-Free Learning of Temporal Point Processes.
Proceedings of the 8th International Conference on Learning Representations, 2020

Continual Learning with Bayesian Neural Networks for Non-Stationary Data.
Proceedings of the 8th International Conference on Learning Representations, 2020

Directional Message Passing for Molecular Graphs.
Proceedings of the 8th International Conference on Learning Representations, 2020

Assessing Box Merging Strategies and Uncertainty Estimation Methods in Multimodel Object Detection.
Proceedings of the Computer Vision - ECCV 2020 Workshops, 2020

Group Centrality Maximization for Large-scale Graphs.
Proceedings of the Symposium on Algorithm Engineering and Experiments, 2020

Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs.
Proceedings of the Conference on Automated Knowledge Base Construction, 2020

2019
Stability and dynamics of communities on online question-answer sites.
Soc. Networks, 2019

Oktoberfest Food Dataset.
CoRR, 2019

Overlapping Community Detection with Graph Neural Networks.
CoRR, 2019

GhostLink: Latent Network Inference for Influence-aware Recommendation.
Proceedings of the World Wide Web Conference, 2019

MLearn: A Declarative Machine Learning Language for Database Systems.
Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, 2019

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Diffusion Improves Graph Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Uncertainty on Asynchronous Time Event Prediction.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Certifiable Robustness to Graph Perturbations.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Certifiable Robustness and Robust Training for Graph Convolutional Networks.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Adversarial Attacks on Node Embeddings via Graph Poisoning.
Proceedings of the 36th International Conference on Machine Learning, 2019

Adversarial Attacks on Graph Neural Networks via Meta Learning.
Proceedings of the 7th International Conference on Learning Representations, 2019

Predict then Propagate: Graph Neural Networks meet Personalized PageRank.
Proceedings of the 7th International Conference on Learning Representations, 2019

Adversarial Attacks on Graph Neural Networks.
Proceedings of the 49. Jahrestagung der Gesellschaft für Informatik, 50 Jahre Gesellschaft für Informatik, 2019

The Power of SQL Lambda Functions.
Proceedings of the Advances in Database Technology, 2019

ML2SQL - Compiling a Declarative Machine Learning Language to SQL and Python.
Proceedings of the Advances in Database Technology, 2019

Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference.
Proceedings of the 56th Annual Design Automation Conference 2019, 2019

In-Database Machine Learning: Gradient Descent and Tensor Algebra for Main Memory Database Systems.
Proceedings of the Datenbanksysteme für Business, 2019

Multi-Source Neural Variational Inference.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Pitfalls of Graph Neural Network Evaluation.
CoRR, 2018

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank.
CoRR, 2018

Mining Contrasting Quasi-Clique Patterns.
CoRR, 2018

Adversarial Attacks on Node Embeddings.
CoRR, 2018

Dual-Primal Graph Convolutional Networks.
CoRR, 2018

Intrinsic Degree: An Estimator of the Local Growth Rate in Graphs.
Proceedings of the Similarity Search and Applications - 11th International Conference, 2018

An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation.
Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

ThingML+: Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning.
Proceedings of MODELS 2018 Workshops: ModComp, 2018

Adversarial Attacks on Neural Networks for Graph Data.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018

NetGAN: Generating Graphs via Random Walks.
Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking.
Proceedings of the 6th International Conference on Learning Representations, 2018

Anomaly Detection in Car-Booking Graphs.
Proceedings of the 2018 IEEE International Conference on Data Mining Workshops, 2018

Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs.
Proc. VLDB Endow., 2017

ZooBP: Belief Propagation for Heterogeneous Networks.
Proc. VLDB Endow., 2017

MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels.
Knowl. Inf. Syst., 2017

Efficient Batched Distance, Closeness and Betweenness Centrality Computation in Unweighted and Weighted Graphs.
Datenbank-Spektrum, 2017

Machine Learning Meets Databases.
Datenbank-Spektrum, 2017

Introduction to Tensor Decompositions and their Applications in Machine Learning.
CoRR, 2017

Personalized Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion.
CoRR, 2017

Deep Gaussian Embedding of Attributed Graphs: Unsupervised Inductive Learning via Ranking.
CoRR, 2017

Detection and Prediction of Natural Hazards Using Large-Scale Environmental Data.
Proceedings of the Advances in Spatial and Temporal Databases, 2017

The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation.
Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

SQL- and Operator-centric Data Analytics in Relational Main-Memory Databases.
Proceedings of the 20th International Conference on Extending Database Technology, 2017

Efficient Batched Distance and Centrality Computation in Unweighted and Weighted Graphs.
Proceedings of the Datenbanksysteme für Business, 2017

2016
Discovery of "comet" communities in temporal and labeled graphs Com<sup>^2</sup>.
Knowl. Inf. Syst., 2016

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection.
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Continuous Experience-aware Language Model.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

EdgeCentric: Anomaly Detection in Edge-Attributed Networks.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016

2015
Linearized and Single-Pass Belief Propagation.
Proc. VLDB Endow., 2015

MultiClust special issue on discovering, summarizing and using multiple clusterings.
Mach. Learn., 2015

Extracting Taxonomies from Bipartite Graphs.
Proceedings of the 24th International Conference on World Wide Web Companion, 2015

Effiziente Integration von Data- und Graph-Mining-Algorithmen in relationale Datenbanksysteme.
Proceedings of the LWA 2015 Workshops: KDML, 2015

Automatic Taxonomy Extraction from Bipartite Graphs.
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Preferential Attachment in Graphs with Affinities.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
GAMer: a synthesis of subspace clustering and dense subgraph mining.
Knowl. Inf. Syst., 2014

KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks.
CoRR, 2014

Linearized and Turbo Belief Propagation.
CoRR, 2014

Robust multivariate autoregression for anomaly detection in dynamic product ratings.
Proceedings of the 23rd International World Wide Web Conference, 2014

Beyond Blocks: Hyperbolic Community Detection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Fault-Tolerant Concept Detection in Information Networks.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

Com2: Fast Automatic Discovery of Temporal ('Comet') Communities.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

SMVC: semi-supervised multi-view clustering in subspace projections.
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

2013
Nesting the earth mover's distance for effective cluster tracing.
Proceedings of the Conference on Scientific and Statistical Database Management, 2013

RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs.
Proceedings of the Conference on Scientific and Statistical Database Management, 2013

Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2013

Stochastic subspace search for top-k multi-view clustering.
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, 2013

Finding contexts of social influence in online social networks.
Proceedings of the 7th Workshop on Social Network Mining and Analysis, 2013

An Evaluation Framework for Temporal Subspace Clustering Approaches.
Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 2013

Spectral Subspace Clustering for Graphs with Feature Vectors.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Mixed Membership Subspace Clustering.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Subspace Clustering for Complex Data.
Proceedings of the Datenbanksysteme für Business, 2013

2012
Subspace clustering for complex data.
PhD thesis, 2012

Tracing Evolving Subspace Clusters in Temporal Climate Data.
Data Min. Knowl. Discov., 2012

Finding density-based subspace clusters in graphs with feature vectors.
Data Min. Knowl. Discov., 2012

Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types.
Proceedings of the Scientific and Statistical Database Management, 2012

Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2012

Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Multi-view clustering using mixture models in subspace projections.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Mining coherent subgraphs in multi-layer graphs with edge labels.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Effective and Robust Mining of Temporal Subspace Clusters.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

A Subspace Clustering Extension for the KNIME Data Mining Framework.
Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012

Assessing the Significance of Data Mining Results on Graphs with Feature Vectors.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data.
Proceedings of the IEEE 28th International Conference on Data Engineering (ICDE 2012), 2012

Tracing clusters in evolving graphs with node attributes.
Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2011
Efficient Processing of Multiple DTW Queries in Time Series Databases.
Proceedings of the Scientific and Statistical Database Management, 2011

DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Tracing Evolving Clusters by Subspace and Value Similarity.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2011

Flexible Fault Tolerant Subspace Clustering for Data with Missing Values.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations.
Proceedings of the EDBT 2011, 2011

Scalable density-based subspace clustering.
Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011

External evaluation measures for subspace clustering.
Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011

A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases.
Proceedings of the Datenbanksysteme für Business, 2011

2010
CoDA: Interactive Cluster Based Concept Discovery.
Proc. VLDB Endow., 2010

MC-Tree: Improving Bayesian Anytime Classification.
Proceedings of the Scientific and Statistical Database Management, 2010

Subspace Clustering for Uncertain Data.
Proceedings of the SIAM International Conference on Data Mining, 2010

Subgraph Mining on Directed and Weighted Graphs.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2010

Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques.
Proceedings of the ICDMW 2010, 2010

MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions.
Proceedings of the ICDMW 2010, 2010

Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms.
Proceedings of the ICDM 2010, 2010

Pattern detector: fast detection of suspicious stream patterns for immediate reaction.
Proceedings of the EDBT 2010, 2010

2009
Evaluating Clustering in Subspace Projections of High Dimensional Data.
Proc. VLDB Endow., 2009

DensEst: Density Estimation for Data Mining in High Dimensional Spaces.
Proceedings of the SIAM International Conference on Data Mining, 2009

Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data.
Proceedings of the ICDM 2009, 2009

Detection of orthogonal concepts in subspaces of high dimensional data.
Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009

High-Dimensional Indexing for Multimedia Features.
Proceedings of the Datenbanksysteme in Business, 2009


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