PyTorch implementation of functions from WAIL and GMMIL

This repository consists of fast implmentations of

1. The Gaussian kernel used in GMMIL
2. The L2 and entropy regularizers used in WAIL

Pre-requisites

• PyTorch (The code is tested on PyTorch 1.2.0.)

Technical Details on WAIL

Technical Details on GMMIL

References

1. Smola, A.; Gretton, A.; Song, L.; and Scholkopf, B. 2007. A Hilbert space embedding for distributions. In International Conference on Algorithmic Learning Theory. Springer.
2. Ramdas, A.; Reddi, S. J.; Poczos, B.; Singh, A.; and Wasserman, L. A. 2015. On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions. In Proceedings of the 29th AAAI Conference on Artificial Intelligence.