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Karsten Borgwardt

30 papers · 2006–2025 · 6 conferences · across top CS/AI conferences

Achievements

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+14 more ↓ πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird
πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (17) 🌟 Keyword Trendsetter Combo (7) πŸ† Keyword Champion πŸ‘‘ Triple Crown 🌱 Topic Pioneer 🧬 Topic Evolution 🀝 Dynamic Duo (10) ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (54) πŸ“ˆ Trend Setter πŸ’Ž Century Club (30) πŸš€ Conference Pioneer

Conferences

NIPS (15) ICLR (6) ICML (5) JMLR (2) IJCAI (1) MLHC (1)

Top co-authors

Bastian Rieck (10) Max Horn (6) Dexiong Chen (6) Michael Moor (5) Christian Bock (4) Arthur Gretton (4) Mahito Sugiyama (3) Alex J. Smola (3) Paolo Pellizzoni (3) Till Hendrik Schulz (3)

Keywords

graph kernel (6) kernel methods (4) graph classification (4) topological data analysis (3) graph neural network (3) high-dimensional datum (2) dimensionality reduction (2) graph isomorphism (2) random walk kernels (2) random walk (2) bayesian inference (2) feature selection (2) reproducing kernel hilbert space (2) persistent homology (2) graph representation learning (2) distribution matching (2) time series classification (2) manifold learning (2) graph representation (2) gaussian process (2)

Papers

Graph Neural Networks Can (Often) Count Substructures ICLR 2025 Learning Long Range Dependencies on Graphs via Random Walks ICLR 2025 On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks NIPS 2024 ProteinShake: Building datasets and benchmarks for deep learning on protein structures NIPS 2023 Fisher Information Embedding for Node and Graph Learning ICML 2023 Unsupervised Manifold Alignment with Joint Multidimensional Scaling ICLR 2023 Weisfeiler and Leman go Machine Learning: The Story so far JMLR 2023 Structure-Aware Transformer for Graph Representation Learning ICML 2022 Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions ICLR 2022 Topological Graph Neural Networks ICLR 2022 Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence NIPS 2020 Set Functions for Time Series ICML 2020 Topological Autoencoders ICML 2020 Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping MLHC 2019 Wasserstein Weisfeiler-Lehman Graph Kernels NIPS 2019 Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology ICLR 2019 A Persistent Weisfeiler-Lehman Procedure for Graph Classification ICML 2019 Finding Statistically Significant Interactions between Continuous Features IJCAI 2019 Finding significant combinations of features in the presence of categorical covariates NIPS 2016 Halting in Random Walk Kernels NIPS 2015 It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals NIPS 2013 Rapid Distance-Based Outlier Detection via Sampling NIPS 2013 Scalable kernels for graphs with continuous attributes NIPS 2013 Feature Selection via Dependence Maximization JMLR 2012 Efficient inference in matrix-variate Gaussian models with \iid observation noise NIPS 2011 Fast subtree kernels on graphs NIPS 2009 Colored Maximum Variance Unfolding NIPS 2007 Correcting Sample Selection Bias by Unlabeled Data NIPS 2006 A Kernel Method for the Two-Sample-Problem NIPS 2006 Fast Computation of Graph Kernels NIPS 2006