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...
Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected st...
Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le ...
Abstract. Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-...
Object matching is a fundamental operation in data analysis. It typically requires the definition of a similarity measure between the classes of objects to be matched. Instead, we...
Optimistic synchronization allows concurrent execution of critical sections while performing dynamic conflict detection and recovery. Optimistic synchronization will increase perf...