1 code implementation • 26 Jan 2024 • Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos Oliver, Karsten Borgwardt

Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules.

1 code implementation • 12 May 2023 • Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt

Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning node embeddings.

1 code implementation • 6 Jul 2022 • Dexiong Chen, Bowen Fan, Carlos Oliver, Karsten Borgwardt

Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input.

1 code implementation • 2 Jun 2022 • Carlos Oliver, Dexiong Chen, Vincent Mallet, Pericles Philippopoulos, Karsten Borgwardt

Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets.

3 code implementations • 7 Feb 2022 • Dexiong Chen, Leslie O'Bray, Karsten Borgwardt

Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them.

Ranked #4 on Graph Property Prediction on ogbg-code2

Emotion Recognition in Conversation Graph Representation Learning

no code implementations • 18 Dec 2021 • Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt

In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data.

no code implementations • 12 Jul 2021 • Michael Moor, Nicolas Bennet, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten Borgwardt

Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.

2 code implementations • ICLR 2022 • Leslie O'Bray, Max Horn, Bastian Rieck, Karsten Borgwardt

Graph generative models are a highly active branch of machine learning.

1 code implementation • ICLR 2022 • Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt

Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles.

1 code implementation • 7 Nov 2020 • Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck

Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis.

1 code implementation • NeurIPS Workshop TDA_and_Beyond 2020 • Michael Moor, Max Horn, Karsten Borgwardt, Bastian Rieck

Topological autoencoders (TopoAE) have demonstrated their capabilities for performing dimensionality reduction while at the same time preserving topological information of the input space.

1 code implementation • 13 Sep 2020 • Jannis Born, Nina Wiedemann, Gabriel Brändle, Charlotte Buhre, Bastian Rieck, Karsten Borgwardt

Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools.

1 code implementation • NeurIPS 2020 • Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, Smita Krishnaswamy

We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.

2 code implementations • 25 May 2020 • Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, Bastian Rieck

The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series.

1 code implementation • 2020 • Christian Hübler, Hans-Peter Kriegel, Karsten Borgwardt, Zoubin Ghahramani

While data mining in chemoinformatics studied graph data with dozens of nodes, systems biology and the Internet are now generating graph data with thousands and millions of nodes.

2 code implementations • ICML 2020 • Max Horn, Michael Moor, Christian Bock, Bastian Rieck, Karsten Borgwardt

Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications.

no code implementations • 29 Aug 2019 • Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Karsten Borgwardt, María Rodríguez Martínez

The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).

1 code implementation • Proceedings of the 36th International Conference on Machine Learning 2019 • Bastian Rieck, Christian Bock, Karsten Borgwardt

The Weisfeiler–Lehman graph kernel exhibits competitive performance in many graph classification tasks.

Ranked #1 on Graph Classification on MUTAG (Mean Accuracy metric)

2 code implementations • NeurIPS 2019 • Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt

Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures.

Ranked #7 on Graph Classification on NCI1

2 code implementations • ICML 2020 • Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt

We propose a novel approach for preserving topological structures of the input space in latent representations of autoencoders.

no code implementations • 16 Apr 2019 • Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.

2 code implementations • 5 Feb 2019 • Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt

This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.

2 code implementations • ICLR 2019 • Bastian Rieck, Matteo Togninalli, Christian Bock, Michael Moor, Max Horn, Thomas Gumbsch, Karsten Borgwardt

While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data.

no code implementations • 28 Feb 2017 • Mahito Sugiyama, Karsten Borgwardt

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing.

1 code implementation • NeurIPS 2016 • Laetitia Papaxanthos, Felipe Llinares-Lopez, Dean Bodenham, Karsten Borgwardt

In high-dimensional settings, where the number of features p is typically much larger than the number of samples n, methods which can systematically examine arbitrary combinations of features, a huge 2^p-dimensional space, have recently begun to be explored.

no code implementations • NeurIPS 2015 • Mahito Sugiyama, Karsten Borgwardt

Random walk kernels measure graph similarity by counting matching walks in two graphs.

no code implementations • 24 Aug 2015 • Felipe Llinares-Lopez, Laetitia Papaxanthos, Dean Bodenham, Karsten Borgwardt

Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem.

no code implementations • NeurIPS 2013 • Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle

Multi-task prediction models are widely being used to couple regressors or classification models by sharing information across related tasks.

no code implementations • NeurIPS 2013 • Mahito Sugiyama, Karsten Borgwardt

Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data.

no code implementations • NeurIPS 2013 • Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt

While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.

no code implementations • 29 Mar 2013 • Aasa Feragen, Jens Petersen, Dominik Grimm, Asger Dirksen, Jesper Holst Pedersen, Karsten Borgwardt, Marleen de Bruijne

Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree.

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