A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery

By Matthew B. Blaschko, Arthur Gretton. 2008

5 citations recorded by the source corpus.

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1. Leila Wehbe
2. Aaditya Ramdas
3. Ian Waudby-Smith
4. Stephan Rabanser
5. Seth R. Flaxman
6. Ruodu Wang
7. Krikamol Muandet

Citations in the corpus

• Nonparametric Independence Testing for Small Sample Sizes (2014, methodology): “on Reproducing Kernel Hilbert Spaces (RKHSs) have become an indispensable part of machine learning. The long list of applications includes kernel PCA [1], dimension reduction [2], structure discovery [3], independence testing [4], Granger causality in time series [5], learning graphical models [6], ”