Covariate Shift and Local Learning by Distribution Matching (2009)
Gretton, A., Smola, A.J., Huang, J., Schmittfull, M., Borgwardt, K.M., Schölkopf, B.
Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve this...
Gene selection via the BAHSIC family of algorithms (2007)
Song, L., Bedo, J., Borgwardt, K.M., Gretton, A., Smola, A.
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, L., Smola, A.J., Gretton, A., Borgwardt, K.M., Bedo, J.
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, L., Smola, A.J., Gretton, A., Borgwardt, K.M.
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...
Correcting Sample Selection Bias by Unlabeled Data (2007)
Huang, J., Smola, A., Gretton, A., Borgwardt, K.M., Schölkopf, B.
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...
A Kernel Method for the Two-Sample-Problem (2007)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.
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...
A Kernel Approach to Comparing Distributions (2007)
Gretton, A., Borgwardt, K.M., Rasch, M., Schölkopf, B., Smola, A.J.
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...
Vishwanathan, S V N, Borgwardt, K. M., 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...
Class prediction from time series gene expression profiles using dynamical systems kernels (2006)
We present a kernel-based approach to the classification of a time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal...