A PyTorch library for differentiable two-sample tests

This package implements a total of six two sample tests:

• The classical Friedman-Rafsky test [FR79].
• The classical k-nearest neighbours (kNN) test [FR83].
• The differentiable Friedman-Rafsky test [DK17].
• The differentiable k-nearest neighbours (kNN) test [DK17].
• The maximum mean discrepancy (MMD) test [GBR+12].
• The energy test [SzekelyR13].

Please refer to the documentation for more information about the project. You can also have a look at the following notebook that showcases how to use the code to train a generative model on MNIST.

After installing PyTorch, you can install the package with:

python setup.py install

To run the tests you simply have to run:

python setup.py test

Note that you will need to have Shogun installed for one of the test cases.

• [DK17] J. Djolonga and A. Krause. Learning Implicit Generative Models Using Differentiable Graph Tests. ArXiv e-prints, September 2017. arXiv:1709.01006.
• [FR79] Jerome H Friedman and Lawrence C Rafsky. Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests. Annals of Statistics, pages 697–717, 1979.
• [FR83] Jerome H Friedman and Lawrence C Rafsky. Graph-theoretic measures of multivariate association and prediction. Annals of Statistics, pages 377–391, 1983.
• [GBR+12] Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. A kernel two-sample test. Journal of Machine Learning Research, 13(Mar):723–773, 2012.
• [SST+12] Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S Zemel, Ruslan R Salakhutdinov, and Ryan P Adams. Cardinality restricted boltzmann machines. In Advances in Neural Information Processing Systems (NIPS), 3293–3301. 2012.
• [SzekelyR13] Gábor J Székely and Maria L Rizzo. Energy statistics: a class of statistics based on distances. Journal of Statistical Planning and Inference, 143(8):1249–1272, 2013.
• [TSZ+12] Daniel Tarlow, Kevin Swersky, Richard S Zemel, Ryan Prescott Adams, and Brendan J Frey. Fast exact inference for recursive cardinality models. Uncertainty in Artificial Intelligence (UAI), 2012.