Description:

ksddescent 0.3

Sampling by optimization of the Kernel Stein Discrepancy
The paper is available at arxiv.org/abs/2105.09994.
The code uses Pytorch, and a numpy backend is available for svgd.


Install
The code is available on pip:
$ pip install ksddescent


Documentation
The documentation is at pierreablin.github.io/ksddescent/.


Example
The main function is ksdd_lbfgs, which uses the fast L-BFGS algorithm to converge quickly.
It takes as input the initial position of the particles, and the score function.
For instance, to samples from a Gaussian (where the score is identity), you can use these simple lines of code:
>>> import torch
>>> from ksddescent import ksdd_lbfgs
>>> n, p = 50, 2
>>> x0 = torch.rand(n, p) # start from uniform distribution
>>> score = lambda x: x # simple score function
>>> x = ksdd_lbfgs(x0, score) # run the algorithm


Reference
If you use this code in your project, please cite:
Anna Korba, Pierre-Cyril Aubin-Frankowski, Simon Majewski, Pierre Ablin
Kernel Stein Discrepancy Descent
International Conference on Machine Learning, 2021


Bug reports
Use the github issue tracker to report bugs.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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