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].
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.