Interpretable Distribution Features with Maximum Testing Power

1 code implementation • NeurIPS 2016 • Wittawat Jitkrittum, Zoltan Szabo, Kacper Chwialkowski, Arthur Gretton

Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i. e, features).

A Kernel Test of Goodness of Fit

1 code implementation • 9 Feb 2016 • Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton

Our test statistic is based on an empirical estimate of this divergence, taking the form of a V-statistic in terms of the log gradients of the target density and the kernel.

Density Estimation

Fast Two-Sample Testing with Analytic Representations of Probability Measures

1 code implementation • NeurIPS 2015 • Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton

The new tests are consistent against a larger class of alternatives than the previous linear-time tests based on the (non-smoothed) empirical characteristic functions, while being much faster than the current state-of-the-art quadratic-time kernel-based or energy distance-based tests.

Two-sample testing Vocal Bursts Valence Prediction

A Wild Bootstrap for Degenerate Kernel Tests

1 code implementation • NeurIPS 2014 • Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed.

Benchmarking Time Series +1

A Kernel Independence Test for Random Processes

1 code implementation • 18 Feb 2014 • Kacper Chwialkowski, Arthur Gretton

A new non parametric approach to the problem of testing the independence of two random process is developed.