Kernel Stein Discrepancy Descent
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 algorithmReference
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.
1 maintainer
Metadata
1 maintainer
Release files for ksddescent 0.3
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