Learning kernels to maximize the power of MMD tests

Last update: Dec 17, 2022

Overview

Code for the paper "Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy" (arXiv:1611.04488; published at ICLR 2017), by Dougal J. Sutherland (@dougalsutherland), Hsiao-Yu Tung, Heiko Strathmann (@karlnapf), Soumyajit De (@lambday), Aaditya Ramdas, Alex Smola, and Arthur Gretton.

Implementations of the variance estimator are in Theano in two_sample/mmd.py and in Tensorflow in gan/mmd.py.
General code for learning kernels for a fixed two-sample test, with Theano, is in two_sample.
Code for the GAN variants, using TensorFlow, is in gan.
Code for the efficient permutation test described in Section 3 is in the 6.0 release of Shogun; look under shogun/src/shogun/statistical_testing. An example of using it in the Python API is in two_sample/mmd_test.py.

This code is under a BSD license, but if you use it, please cite the paper.

Owner

Danica J. Sutherland

Machine learning professor.

GitHub Repository https://arxiv.org/abs/1611.04488

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Learning kernels to maximize the power of MMD tests

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