In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connecti...
Le Song, Jonathan Huang, Alexander J. Smola, Kenji...
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 reprodu...
Alexander J. Smola, Arthur Gretton, Le Song, Bernh...
A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing, and independence testing....
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fu...
A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing and independence testing. ...
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fu...
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this c...
Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smo...