Search Results for author: Karsten Borgwardt

Found 29 papers, 18 papers with code

Unsupervised Manifold Alignment with Joint Multidimensional Scaling

1 code implementation6 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.

Domain Adaptation Graph Matching

Approximate Network Motif Mining Via Graph Learning

1 code implementation2 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.

BIG-bench Machine Learning Graph Classification +1

Structure-Aware Transformer for Graph Representation Learning

2 code implementations7 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.

Graph Representation Learning

Weisfeiler and Leman go Machine Learning: The Story so far

no code implementations18 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.

BIG-bench Machine Learning Representation Learning

Topological Graph Neural Networks

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.

Graph Learning Node Classification

Graph Kernels: State-of-the-Art and Future Challenges

1 code implementation7 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.


Challenging Euclidean Topological Autoencoders

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.

Dimensionality Reduction

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

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.

Path Imputation Strategies for Signature Models of Irregular Time Series

2 code implementations25 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.

Imputation Irregular Time Series +1

Metropolis Algorithms for Representative Subgraph Sampling

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.

Set Functions for Time Series

1 code implementation 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.

Time Series Analysis Time Series Classification

PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning

no code implementations29 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).

reinforcement-learning Reinforcement Learning (RL)

Wasserstein Weisfeiler-Lehman Graph Kernels

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.

Graph Classification

Topological Autoencoders

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.

Topological Data Analysis

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

2 code implementations5 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.

Dynamic Time Warping Management +2

Finding Statistically Significant Interactions between Continuous Features

no code implementations28 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.

Finding significant combinations of features in the presence of categorical covariates

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.


Searching for significant patterns in stratified data

no code implementations24 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.

Scalable kernels for graphs with continuous attributes

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.

General Classification

Rapid Distance-Based Outlier Detection via Sampling

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.

Outlier Detection

Geometric tree kernels: Classification of COPD from airway tree geometry

no code implementations29 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.

Classification General Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.