Code & Data

Below you find an overview of the supplementary material (Code, Data, Appendices, ...) for some of our recent works:

• Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
• Expressivity and Generalization: Fragment-Biases for Molecular GNNs (ICML 2024)
• Uncertainty for Active Learning on Graphs (ICML 2024)
• From Zero to Turbulence: Generative Modeling for 3D Flow Simulation (ICLR 2024)
• Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More (NeurIPS 2023)
• Generalizing Neural Wave Functions (ICML 2023)
• Uncertainty Estimation for Molecules: Desiderata and Methods (ICML 2023)
• Ewald-based Long-Range Message Passing for Molecular Graphs (ICML 2023)
• Localized Randomized Smoothing for Collective Robustness Certification (ICLR 2023)
• Sampling-free Inference of Ab-initio Potential Energy Surface Networks (ICLR 2023)
• Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks (NeurIPS 2022)
• Invariance-Aware Randomized Smoothing Certificates (NeurIPS 2022)
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions (ICLR 2022)
• Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness (ICLR 2022)
• Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks (ICLR 2022)
• Neural Flows: Efficient Alternative to Neural ODEs (NeurIPS 2021)
• Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)
• Robustness of Graph Neural Networks at Scale (NeurIPS 2021)
• GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS 2021)
• Directional Message Passing on Molecular Graphs via Synthetic Coordinates (NeurIPS 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)
• Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
• Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions
• Reliable Graph Neural Networks via Robust Aggregation (NeurIPS 2020)
• Fast and Flexible Temporal Point Processes with Triangular Maps (NeurIPS 2020)
• Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (NeurIPS 2020)
• Efficient Robustness Certificates for Discrete Data (ICML 2020)
• Scaling Graph Neural Networks with Approximate PageRank (KDD 2020)
• Intensity-Free Learning of Temporal Point Processes (ICLR 2020)
• Directional Message Passing for Molecular Graphs (ICLR 2020)
• Certifiable Robustness to Graph Perturbations (NeurIPS 2019)
• Uncertainty on Asynchronous Time Event Prediction (Neurips 2019)
• Diffusion Improves Graph Learning (NeurIPS 2019)
• Adversarial Attacks on Node Embeddings via Graph Poisoning (ICML 2019)
• Certifiable Robustness and Robust Training for Graph Convolutional Networks (KDD 2019)
• Overlapping Community Detection with Graph Neural Networks (KDD 2019 Deep Learning on Graphs Workshop)
• Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019)
• Adversarial Attacks on Graph Neural Networks via Meta Learning (ICLR 2019)
• Pitfalls of Graph Neural Network Evaluation (NeurIPS 2018 Relational Representation Learning Workshop)
• Adversarial Attacks on Neural Networks for Graph Data (KDD 2018)
• NetGAN: Generating Graphs via Random Walks (ICML 2018)
• Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (ICLR 2018)
• Bayesian Robust Attributed Graph Clustering: Joint Learning of Partial Anomalies and Group Structure (AAAI 2018)
• Robust Spectral Clustering (KDD 2017)
• Hyperbolae Are No Hyperbole: Modelling Communities That Are Not Cliques (ICDM 2016)