Stephan Günnemann
117 publications
19 venues
H Index 30
Affiliation
Technical University of Munich, GermanyCarnegie Mellon University, Pittsburgh, USA
Links
| Name | Venue | Year | citations |
|---|---|---|---|
Expressivity and Generalization: Fragment-Biases for Molecular GNNs.
|
ICML | 2024 | 0 |
| Uncertainty for Active Learning on Graphs.
|
ICML | 2024 | 0 |
| From Zero to Turbulence: Generative Modeling for 3D Flow Simulation.
|
ICLR | 2024 | 0 |
| Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems.
|
IEEE Robotics and Automation Letters | 2024 | 0 |
| Generalized density attractor clustering for incomplete data.
|
DMKD | 2023 | 0 |
| Uncertainty Estimation for Molecules: Desiderata and Methods.
|
ICML | 2023 | 0 |
| Transformers Meet Directed Graphs.
|
ICML | 2023 | 0 |
| Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion.
|
ICML | 2023 | 0 |
| Generalizing Neural Wave Functions.
|
ICML | 2023 | 0 |
| Ewald-based Long-Range Message Passing for Molecular Graphs.
|
ICML | 2023 | 0 |
| Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models.
|
AAMAS | 2023 | 0 |
| Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning.
|
CoRL | 2023 | 0 |
| Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
|
ECML/PKDD | 2023 | 0 |
| Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions.
|
NIPS/NeurIPS | 2023 | 0 |
| Add and Thin: Diffusion for Temporal Point Processes.
|
NIPS/NeurIPS | 2023 | 0 |
| (Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More.
|
NIPS/NeurIPS | 2023 | 0 |
| Hierarchical Randomized Smoothing.
|
NIPS/NeurIPS | 2023 | 0 |
| Localized Randomized Smoothing for Collective Robustness Certification.
|
ICLR | 2023 | 0 |
| Sampling-free Inference for Ab-Initio Potential Energy Surface Networks.
|
ICLR | 2023 | 0 |
| Unveiling the sampling density in non-uniform geometric graphs.
|
ICLR | 2023 | 0 |
| Revisiting Robustness in Graph Machine Learning.
|
ICLR | 2023 | 0 |
| Differentiable DAG Sampling.
|
ICLR | 2022 | 6 |
| Robustness verification of ReLU networks via quadratic programming.
|
MLJ | 2022 | 0 |
| Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution.
|
NIPS/NeurIPS | 2022 | 0 |
| Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks.
|
AAAI | 2022 | 0 |
| Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions.
|
ICLR | 2022 | 12 |
| Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks.
|
ICLR | 2022 | 11 |
| End-to-End Learning of Probabilistic Hierarchies on Graphs.
|
ICLR | 2022 | 0 |
| Winning the Lottery Ahead of Time: Efficient Early Network Pruning.
|
ICML | 2022 | 1 |
| Intriguing Properties of Input-Dependent Randomized Smoothing.
|
ICML | 2022 | 0 |
| 3D Infomax improves GNNs for Molecular Property Prediction.
|
ICML | 2022 | 0 |
| Invariance-Aware Randomized Smoothing Certificates.
|
NIPS/NeurIPS | 2022 | 0 |
| Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks.
|
NIPS/NeurIPS | 2022 | 0 |
| Are Defenses for Graph Neural Networks Robust?
|
NIPS/NeurIPS | 2022 | 0 |
| Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness.
|
ICLR | 2022 | 0 |
| Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions.
|
ICLR | 2022 | 0 |
| Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation.
|
DMKD | 2022 | 0 |
| Scalable Normalizing Flows for Permutation Invariant Densities.
|
ICML | 2021 | 0 |
| Neural Flows: Efficient Alternative to Neural ODEs.
|
NIPS/NeurIPS | 2021 | 12 |
| Mining communities and their descriptions on attributed graphs: a survey.
|
DMKD | 2021 | 7 |
| GemNet: Universal Directional Graph Neural Networks for Molecules.
|
NIPS/NeurIPS | 2021 | 121 |
| Robustness of Graph Neural Networks at Scale.
|
NIPS/NeurIPS | 2021 | 27 |
| Graphhopper: Multi-hop Scene Graph Reasoning for Visual Question Answering.
|
ISWC | 2021 | 10 |
| Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions.
|
AISTATS | 2021 | 9 |
| Language-Agnostic Representation Learning of Source Code from Structure and Context.
|
ICLR | 2021 | 69 |
| Neural Temporal Point Processes: A Review.
|
IJCAI | 2021 | 30 |
| Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification.
|
NIPS/NeurIPS | 2021 | 13 |
| Detecting Anomalous Event Sequences with Temporal Point Processes.
|
NIPS/NeurIPS | 2021 | 1 |
| Directional Message Passing on Molecular Graphs via Synthetic Coordinates.
|
NIPS/NeurIPS | 2021 | 11 |
| Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More.
|
ICML | 2021 | 5 |
| Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
|
ICML | 2021 | 0 |
| Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks.
|
ICLR | 2021 | 0 |
| Reachable sets of classifiers and regression models: (non-)robustness analysis and robust training.
|
MLJ | 2021 | 0 |
| Directional Message Passing for Molecular Graphs.
|
ICLR | 2020 | 404 |
| Scaling Graph Neural Networks with Approximate PageRank.
|
KDD | 2020 | 156 |
| Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations.
|
KDD | 2020 | 30 |
| Gauss Shift: Density Attractor Clustering Faster Than Mean Shift.
|
ECML/PKDD | 2020 | 1 |
| Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More.
|
ICML | 2020 | 40 |
| Reliable Graph Neural Networks via Robust Aggregation.
|
NIPS/NeurIPS | 2020 | 35 |
| Continual Learning with Bayesian Neural Networks for Non-Stationary Data.
|
ICLR | 2020 | 38 |
| Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts.
|
NIPS/NeurIPS | 2020 | 48 |
| Deep Rao-Blackwellised Particle Filters for Time Series Forecasting.
|
NIPS/NeurIPS | 2020 | 22 |
| Fast and Flexible Temporal Point Processes with Triangular Maps.
|
NIPS/NeurIPS | 2020 | 16 |
| Intensity-Free Learning of Temporal Point Processes.
|
ICLR | 2020 | 0 |
| Diffusion Improves Graph Learning.
|
NIPS/NeurIPS | 2019 | 310 |
| Certifiable Robustness to Graph Perturbations.
|
NIPS/NeurIPS | 2019 | 76 |
| Uncertainty on Asynchronous Time Event Prediction.
|
NIPS/NeurIPS | 2019 | 20 |
| Certifiable Robustness and Robust Training for Graph Convolutional Networks.
|
KDD | 2019 | 88 |
| GhostLink: Latent Network Inference for Influence-aware Recommendation.
|
WWW | 2019 | 7 |
| Multi-Source Neural Variational Inference.
|
AAAI | 2019 | 0 |
| Adversarial Attacks on Node Embeddings via Graph Poisoning.
|
ICML | 2019 | 0 |
| Adversarial Attacks on Neural Networks for Graph Data.
|
IJCAI | 2019 | 0 |
| Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift.
|
NIPS/NeurIPS | 2019 | 0 |
| Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping.
|
ECML/PKDD | 2018 | 4 |
| An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors.
|
SDM | 2018 | 18 |
| NetGAN: Generating Graphs via Random Walks.
|
ICML | 2018 | 251 |
| Adversarial Attacks on Neural Networks for Graph Data.
|
KDD | 2018 | 613 |
| Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure.
|
AAAI | 2018 | 53 |
| Making Kernel Density Estimation Robust towards Missing Values in Highly Incomplete Multivariate Data without Imputation.
|
SDM | 2018 | 5 |
| Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings.
|
KDD | 2017 | 50 |
| The Power of Certainty: A Dirichlet-Multinomial Model for Belief Propagation.
|
SDM | 2017 | 9 |
| Continuous Experience-aware Language Model.
|
KDD | 2016 | 5 |
| Hyperbolae are No Hyperbole: Modelling Communities That are Not Cliques.
|
ICDM | 2016 | 6 |
| BIRDNEST: Bayesian Inference for Ratings-Fraud Detection.
|
SDM | 2016 | 0 |
| Automatic Taxonomy Extraction from Bipartite Graphs.
|
ICDM | 2015 | 0 |
| Preferential Attachment in Graphs with Affinities.
|
AISTATS | 2015 | 13 |
| MultiClust special issue on discovering, summarizing and using multiple clusterings.
|
MLJ | 2015 | 0 |
| Robust multivariate autoregression for anomaly detection in dynamic product ratings.
|
WWW | 2014 | 56 |
| Com2: Fast Automatic Discovery of Temporal ('Comet') Communities.
|
PAKDD | 2014 | 92 |
| Fault-Tolerant Concept Detection in Information Networks.
|
PAKDD | 2014 | 0 |
| SMVC: semi-supervised multi-view clustering in subspace projections.
|
KDD | 2014 | 21 |
| Beyond Blocks: Hyperbolic Community Detection.
|
ECML/PKDD | 2014 | 37 |
| Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution.
|
KDD | 2014 | 55 |
| Spectral Subspace Clustering for Graphs with Feature Vectors.
|
ICDM | 2013 | 61 |
| Mixed Membership Subspace Clustering.
|
ICDM | 2013 | 3 |
| Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors.
|
PAKDD | 2013 | 44 |
| Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases.
|
PAKDD | 2012 | 7 |
| Multi-view clustering using mixture models in subspace projections.
|
KDD | 2012 | 35 |
| Effective and Robust Mining of Temporal Subspace Clusters.
|
ICDM | 2012 | 7 |
| Finding density-based subspace clusters in graphs with feature vectors.
|
DMKD | 2012 | 1 |
| Tracing clusters in evolving graphs with node attributes.
|
CIKM | 2012 | 10 |
| Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data.
|
KDD | 2012 | 17 |
| Tracing Evolving Subspace Clusters in Temporal Climate Data.
|
DMKD | 2012 | 24 |
| Assessing the Significance of Data Mining Results on Graphs with Feature Vectors.
|
ICDM | 2012 | 3 |
| Mining coherent subgraphs in multi-layer graphs with edge labels.
|
KDD | 2012 | 0 |
| Tracing Evolving Clusters by Subspace and Value Similarity.
|
PAKDD | 2011 | 16 |
| External evaluation measures for subspace clustering.
|
CIKM | 2011 | 56 |
| Scalable density-based subspace clustering.
|
CIKM | 2011 | 24 |
| DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors.
|
ECML/PKDD | 2011 | 63 |
| Flexible Fault Tolerant Subspace Clustering for Data with Missing Values.
|
ICDM | 2011 | 19 |
| Subgraph Mining on Directed and Weighted Graphs.
|
PAKDD | 2010 | 16 |
| Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms.
|
ICDM | 2010 | 125 |
| Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data.
|
ICDM | 2010 | 72 |
| Subspace Clustering for Uncertain Data.
|
SDM | 2010 | 34 |
| DensEst: Density Estimation for Data Mining in High Dimensional Spaces.
|
SDM | 2009 | 46 |
| Detection of orthogonal concepts in subspaces of high dimensional data.
|
CIKM | 2009 | 47 |
| Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data.
|
ICDM | 2009 | 99 |