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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.

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Learning kernels to maximize the power of MMD tests

arxiv.org/abs/1611.04488

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generative-adversarial-networkhypothesis-testingkernel-methodsmachine-learningmaximum-mean-discrepancypythonshogunstatistical-teststensorflowtheano

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