← Authors

Stephan Günnemann

94 papers · 2015–2025 · 11 conferences · across top CS/AI conferences

Achievements

🏃 Academic Marathon (10) 🗺️ Taxonomy Completionist (19) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (11) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (11) 🏠 Conference Loyalist (31) 👑 Triple Crown 🌱 Topic Pioneer 🏆 Grand Slam 🔬 Deep Specialist (19) 🤝 Dynamic Duo (16) 🏆 Keyword Champion (3) 📈 Trend Setter ❓ The Questioner (2) ⚡ Prolific Year (9) 🚀 Conference Pioneer 🗃️ Keyword Collector (60) 💎 Century Club (94) 🔥 Unstoppable (8)

Conferences

NIPS (31) ICLR (28) ICML (23) AAAI (2) AISTATS (2) ICCV (2) IJCAI (2) CORL (1) CVPR (1) UAI (1) WACV (1)

Top co-authors

Simon Geisler (16) Aleksandar Bojchevski (15) Daniel Zügner (14) Bertrand Charpentier (12) Johannes Gasteiger (11) Nicholas Gao (9) Jan Schuchardt (9) Tom Wollschläger (9) Marin Biloš (7) Leo Schwinn (6)

Research topics

Differential Privacy (1)

Keywords

graph neural network (20) out-of-distribution detection (10) adversarial robustness (9) randomized smoothing (7) adversarial attack (7) message passing (5) temporal point process (4) uncertainty estimation (4) diffusion model (4) certified robustness (4) normalizing flow (4) anomaly detection (4) uncertainty quantification (4) gaussian process (3) representation learning (3) node classification (3) density estimation (3) molecular property prediction (3) variational inference (2) geometric deep learning (2)

Papers

Joint Out-of-Distribution Filtering and Data Discovery Active Learning CVPR 2025
Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects using Prototypes WACV 2025
The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence ICML 2025
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting ICML 2025
Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation ICML 2025
UnHiPPO: Uncertainty-aware Initialization for State Space Models ICML 2025
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging ICML 2025
REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective ICML 2025
Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory ICML 2025
A Probabilistic Perspective on Unlearning and Alignment for Large Language Models ICLR 2025
Learning Equivariant Non-Local Electron Density Functionals ICLR 2025
MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds ICLR 2025
Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance ICLR 2025
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting ICLR 2025
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning ICLR 2025
Exact Certification of (Graph) Neural Networks Against Label Poisoning ICLR 2025
Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space ICLR 2025
Unlocking Point Processes through Point Set Diffusion ICLR 2025
GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation ICCV 2025
Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation ICCV 2025
Expressivity and Generalization: Fragment-Biases for Molecular GNNs ICML 2024
Efficient Adversarial Training in LLMs with Continuous Attacks NIPS 2024
Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space NIPS 2024
Expected Probabilistic Hierarchies NIPS 2024
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood NIPS 2024
Energy-based Epistemic Uncertainty for Graph Neural Networks NIPS 2024
Spatio-Spectral Graph Neural Networks NIPS 2024
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification NIPS 2024
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations NIPS 2024
Unified Guidance for Geometry-Conditioned Molecular Generation NIPS 2024
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation ICLR 2024
Uncertainty for Active Learning on Graphs ICML 2024
Guaranteeing Robustness Against Real-World Perturbations In Time Series Classification Using Conformalized Randomized Smoothing UAI 2024
Transformers Meet Directed Graphs ICML 2023
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More NIPS 2023
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions NIPS 2023
Uncertainty Estimation for Molecules: Desiderata and Methods ICML 2023
Ewald-based Long-Range Message Passing for Molecular Graphs ICML 2023
Generalizing Neural Wave Functions ICML 2023
Unveiling the sampling density in non-uniform geometric graphs ICLR 2023
Revisiting Robustness in Graph Machine Learning ICLR 2023
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks ICLR 2023
Localized Randomized Smoothing for Collective Robustness Certification ICLR 2023
Hierarchical Randomized Smoothing NIPS 2023
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion ICML 2023
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning CORL 2023
Add and Thin: Diffusion for Temporal Point Processes NIPS 2023
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution NIPS 2022
Intriguing Properties of Input-Dependent Randomized Smoothing ICML 2022
3D Infomax improves GNNs for Molecular Property Prediction ICML 2022
Winning the Lottery Ahead of Time: Efficient Early Network Pruning ICML 2022
Domain Reconstruction for UWB Car Key Localization Using Generative Adversarial Networks AAAI 2022
Differentiable DAG Sampling ICLR 2022
End-to-End Learning of Probabilistic Hierarchies on Graphs ICLR 2022
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness ICLR 2022
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions ICLR 2022
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions ICLR 2022
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks ICLR 2022
Invariance-Aware Randomized Smoothing Certificates NIPS 2022
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks NIPS 2022
Are Defenses for Graph Neural Networks Robust? NIPS 2022
Scalable Normalizing Flows for Permutation Invariant Densities ICML 2021
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions AISTATS 2021
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks ICLR 2021
Language-Agnostic Representation Learning of Source Code from Structure and Context ICLR 2021
Neural Temporal Point Processes: A Review IJCAI 2021
Neural Flows: Efficient Alternative to Neural ODEs NIPS 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification NIPS 2021
Directional Message Passing on Molecular Graphs via Synthetic Coordinates NIPS 2021
Detecting Anomalous Event Sequences with Temporal Point Processes NIPS 2021
Robustness of Graph Neural Networks at Scale NIPS 2021
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More ICML 2021
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021
GemNet: Universal Directional Graph Neural Networks for Molecules NIPS 2021
Fast and Flexible Temporal Point Processes with Triangular Maps NIPS 2020
Directional Message Passing for Molecular Graphs ICLR 2020
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More ICML 2020
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting NIPS 2020
Reliable Graph Neural Networks via Robust Aggregation NIPS 2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts NIPS 2020
Intensity-Free Learning of Temporal Point Processes ICLR 2020
Continual Learning with Bayesian Neural Networks for Non-Stationary Data ICLR 2020
Multi-Source Neural Variational Inference AAAI 2019
Adversarial Attacks on Graph Neural Networks via Meta Learning ICLR 2019
Predict then Propagate: Graph Neural Networks meet Personalized PageRank ICLR 2019
Uncertainty on Asynchronous Time Event Prediction NIPS 2019
Diffusion Improves Graph Learning NIPS 2019
Adversarial Attacks on Node Embeddings via Graph Poisoning ICML 2019
Certifiable Robustness to Graph Perturbations NIPS 2019
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift NIPS 2019
Adversarial Attacks on Neural Networks for Graph Data IJCAI 2019
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking ICLR 2018
NetGAN: Generating Graphs via Random Walks ICML 2018
Preferential Attachment in Graphs with Affinities AISTATS 2015