A kernel method for unsupervised network inference (2009)
Lippert, Christoph, Stegle, Oliver, Ghahramani, Zoubin, Borgwardt, Karsten
Network inference is the problem of inferring edges between a set of real-world objects, for instance, between pairs of proteins in bioinformatics. Current kernel-based approaches to this problem all...
A kernel method for unsupervised structured network inference (2008)
Lippert, Christoph, Stegle, Oliver, Ghahramani, Zoubin, Borgwardt, Karsten
Network inference is the problem of inferring edges between a set of real-world objects, for instance, interactions between pairs of proteins in bioinformatics. Current kernel-based approaches to...
Efficient Graphlet Kernels for Large Graph Comparison (2008)
Shervashidze, Nino, Vishwanathan, S V N, Petri, Tobias, Mehlhorn, Kurt, Borgwardt, Karsten
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting common {\it graphlets}, \ie...
Near-optimal supervised feature selection among frequent subgraphs (2008)
Thoma, Marisa, Cheng, Hong, Gretton, Arthur, Han, Jiawei, Kriegel, Hans-Peter, Smola, Alex, ...
Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and anomaly detection in...
Stegle, Oliver, Denby, Katherine, Ghahramani, Zoubin, Wild, David, Borgwardt, Karsten
Understanding the regulatory mechanisms that are responsible for an organism's response to environmental changes is an important question in molecular biology. A first and important step towards this...
A Kernel Method for the Two-Sample Problem (2008)
Gretton, Arthur, Borgwardt, Karsten, Rasch, Malte J., Scholkopf, Bernhard, Smola, Alexander J.
We propose a framework for analyzing and comparing distributions, allowing us to design statistical tests to determine if two samples are drawn from different distributions. Our test statistic is the...
Bernhard Schölkopf, Karsten Borgwardt, Kenji Fukumizu, Arthur Gretton, Jiayuan Huang, Quoc Le, ...
An example of a kernel algorithm, revisited µ(X)
Le Song, Alex Smola, Arthur Gretton, Karsten Borgwardt, Kl Tr(kl, Hsic(f G Pr
Proof. Define the Pochammer symbol as (m)n = m!
Le Song, Arthur Gretton, Alex Smola, Karsten Borgwardt
We introduce a framework of feature filtering for supervised learning. It employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between data and labels. The key idea is...
Colored Maximum Variance Unfolding (2008)
Le Song, Alex Smola, Karsten Borgwardt, Arthur Gretton
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while...
Colored Maximum Variance Unfolding (2008)
Le Song, Alex Smola, Karsten Borgwardt, Arthur Gretton
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while...
The skew spectrum of graphs (2008)
Kondor, Risi, Borgwardt, Karsten
The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We present a new system...
Gene selection via the BAHSIC family of algorithms (2007)
Song, Le, Bedo, Justin, Borgwardt, Karsten, Gretton, Arthur, Smola, Alex
Motivation: Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are...
Supervised Feature Selection via Dependence Estimation (2007)
Song, Le, Smola, Alex, Gretton, Arthur, Borgwardt, Karsten, Bedo, Justin
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that...
A Dependence Maximization View of Clustering (2007)
Song, Le, Smola, Alex, Gretton, Arthur, Borgwardt, Karsten
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion...
Supervised Feature Selection via Dependence Estimation (2007)
Song, Le, Smola, Alex, Gretton, Arthur, Borgwardt, Karsten, Bedo, Justin
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that...
Colored Maximum Variance Unfolding (2007)
Song, Le, Smola, Alex, Borgwardt, Karsten, Gretton, Arthur
Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while...
Fast Computation of Graph Kernels (2007)
Vishwanathan, S V N, Borgwardt, Karsten, Schraudolph, Nicol
Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us...
CLASS PREDICTION FROM TIME SERIES GENE EXPRESSION PROFILES USING DYNAMICAL SYSTEMS KERNELS (2006)
Borgwardt, Karsten, Vishwanathan, S V N, Kriegel, Hans-Peter
We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal...
Large Protein function prediction via faster graph kernels (2005)
Borgwardt, Karsten, Vishwanathan, S V N, Schraudolph, Nicol, Kriegel, Hans-Peter
Kernel functions on graphs have been defined over recent years. In earlier work, we have employed random walk graph kernels for predicting protein function from graph representations that integrate...
Vishwanathan, S V N, Borgwardt, Karsten, Guttman, Omri, Smola, Alex
We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings (LLE) to unseen data. We also present a principled method to combine...
Protein function prediction via graph kernels (2005)
Borgwardt, Karsten, Ong, Cheng Soon, Schonauer, Stefan, Vishwanathan, S V N, Smola, Alex, Kriegel, Hans-Peter
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid...