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6 papers · 1 filter
GP-select: Accelerating EM using adaptive subspace preselection
Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai +2
We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative lat…
A Wild Bootstrap for Degenerate Kernel Tests
Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton
A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests…
A low variance consistent test of relative dependency
Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus +1
We describe a novel non-parametric statistical hypothesis test of relative dependence between a source variable and two candidate target variables. Such a test enables us to determ…
Mondrian Forests: Efficient Online Random Forests
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random fo…
Kernel Mean Shrinkage Estimators
Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu +2
A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principa…
A Kernel Independence Test for Random Processes
Kacper Chwialkowski, Arthur Gretton
A new non parametric approach to the problem of testing the independence of two random process is developed. The test statistic is the Hilbert Schmidt Independence Criterion (HSIC)…