Bayesian two-sample tests (2009)
Borgwardt, Karsten M., Ghahramani, Zoubin
In this paper, we present two classes of Bayesian approaches to the two-sample problem. Our first class of methods extends the Bayesian t-test to include all parametric models in the exponential...
Karsten M. Borgwardt, Alex Smola
Abstract. We present a principled method to combine kernels under joint regularization constraints. Central to our method is an extension of the representer theorem for handling multiple joint...
Efficient Graphlet Kernels for Large Graph Comparison (2008)
Shervashidze, Nino, Vishwanathan, S.V.N., Petri, Tobias H., Mehlhorn, Kurt, Borgwardt, Karsten M.
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we attempt to rectify this situation. We compare graphs by counting common...
Jiayuan Huang, Karsten M. Borgwardt, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover...
Vishwanathan, S. V. N., Borgwardt, Karsten M., Kondor, Imre Risi, Schraudolph, Nicol N.
We present a unified framework to study graph kernels, special cases of which include the random walk graph kernel \citep{GaeFlaWro03,BorOngSchVisetal05}, marginalized graph kernel...
An Efficient Sampling Scheme For Comparison of Large Graphs (2008)
Karsten M. Borgwardt, Hans-peter Kriegel
As new graph structured data is being generated, graph comparison has become an important and challenging problem in application areas such as molecular biology, telecommunications, chemoinformatics,...
Jiayuan Huang, Karsten M. Borgwardt, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover...
Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alex Smola
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...
BIOINFORMATICS Gene Selection via the BAHSIC Family of Algorithms (2008)
Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alex Smola
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...
Arthur Gretton, Bernhard Schölkopf, Karsten M. Borgwardt, Malte Rasch, Alexander J. Smola
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a...
Jiayuan Huang, Karsten M. Borgwardt, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover...
Arthur Gretton, Bernhard Schölkopf, Karsten M. Borgwardt, Malte Rasch, Alexander J. Smola
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a...
ABSTRACT 3DString: A Feature String Kernel for 3D Object Classification on Voxelized Data (2008)
Johannes Aßfalg, Karsten M. Borgwardt, Hans-peter Kriegel
Classification of 3D objects remains an important task in many areas of data management such as engineering, medicine or biology. As a common preprocessing step in current approaches to...
Arthur Gretton, Bernhard Schölkopf, Karsten M. Borgwardt, Malte Rasch, Alexander J. Smola
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a...
Supervised Feature Selection via Dependence Estimation (2008)
Le Song, Alex Smola, Arthur Gretton, Karsten M. Borgwardt
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...
Jiayuan Huang, Karsten M. Borgwardt, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover...
Jiayuan Huang, Karsten M. Borgwardt, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf
We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover...
Karsten M. Borgwardt, Hanspeter Kriegel I
From a computer scientist's point of view, protein function prediction can be regarded as a classification problem, ...
Karsten M. Borgwardt, Omri Guttman, Alex Smola
Abstract. We present a principled method to combine kernels under joint regularization constraints. Central to our method is an extension of the representer theorem for handling multiple joint...
A Kernel Approach to Comparing Distributions (2007)
Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte, Schölkopf, Bernhard, Smola, Alex
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert...
A Kernel Approach to Comparing Distributions (2007)
Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte, Schölkopf, Bernhard, Smola, Alex
We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a Reproducing Kernel Hilbert...
A Kernel Method for the Two-Sample-Problem (2007)
Gretton, Arthur, Borgwardt, Karsten M., Rasch, Malte, Schölkopf, Bernhard, Smola, Alex
We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a...
Correcting sample selection bias by unlabeled data (2007)
Jiayuan Huang, Arthur Gretton, Bernhard Schölkopf, Alexander J. Smola, Karsten M. Borgwardt
Graph kernels for disease outcome prediction from protein-protein interaction networks (2007)
Karsten M. Borgwardt, Hans-peter Kriegel, N. Schraudolph
It is widely believed that comparing discrepancies in the protein-protein interaction (PPI) networks of individuals will become an important tool in understanding and preventing diseases. Currently...
Graph kernels for disease outcome prediction from protein-protein interaction networks (2007)
Karsten M. Borgwardt, Hans-peter Kriegel, N. Schraudolph
It is widely believed that comparing discrepancies in the protein-protein interaction (PPI) networks of individuals will become an important tool in understanding and preventing diseases. Currently...
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006)
Borgwardt, Karsten M., Gretton, Arthur, Rasch, Malte, Kriegel, Hans-Peter, Schölkopf, Bernhard, Smola, Alex
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based...
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006)
Borgwardt, Karsten M., Gretton, Arthur, Rasch, Malte, Kriegel, Hans-Peter, Schölkopf, Bernhard, Smola, Alex
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based...
Integrating structured biological data by kernel maximum mean discrepancy (2006)
Karsten M. Borgwardt, A Arthur Gretton, Hans-peter Kriegel, A Bernhard Schölkopf
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based...
Integrating structured biological data by kernel maximum mean discrepancy (2006)
Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-peter Kriegel, Bernhard Schölkopf, Alex J. Smola
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based...
Pattern mining in frequent dynamic subgraphs (2006)
Karsten M. Borgwardt, Hans-peter Kriegel, Peter Wackersreuther
Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowledge. While current...
Fast computation of graph kernels (2006)
Karsten M. Borgwardt, Nicol N. Schraudolph
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)
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...
Fast computation of graph kernels (2006)
Karsten M. Borgwardt, Nicol N. Schraudolph
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)
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...
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006)
Borgwardt, Karsten M., Gretton, Arthur, Rasch, Malte J., Kriegel, Hans-Peter, Schölkopf, Bernhard, Smola, Alex J.
Motivation: Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based...
Protein function prediction via graph kernels (2005)
Borgwardt, Karsten M., Ong, Cheng Soon, Schoenauer, 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...
Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, Alex J. Smola, Hans-peter Kriegel
Vol. 21 Suppl. 1 2005, pages i47–i56 doi:10.1093/bioinformatics/bti1007
Karsten M. Borgwardt, Omri Guttman, Alex Smola
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...
Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, Alex J. Smola, Hans-peter Kriegel
Vol. 21 Suppl. 1 2005, pages i1–i10 doi:10.1093/bioinformatics/bti1007 Protein function prediction via graph kernels
Protein function prediction via graph kernels (2005)
Borgwardt, Karsten M., Ong, Cheng Soon, Schönauer, Stefan, Vishwanathan, S. V. N., Smola, Alex J., 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...