Readme

Official implementation of the paper LSB: Local Self-Balancinng MCMC in Discrete Spaces accepted for presentation at ICML 2022.

Prerequisites

Checking/Installing prerequisite libraries:

python 3.8.5
numpy 1.19.5
statsmodels 0.13.0
pgmpy 0.1.16
torch 1.8.1

Other libraries include matplotlib, seaborn, pandas, tqdm and tensorflow_probability.

Experiments on 2D Ising

To run the simulations, run the bash script runner.sh

To evaluate the sampler, run the script eval.sh

Experiments on RBM

Code adapted from Gibbs-With-Gradients, ICML 2021. To run the simulations, run the bash script rbm_sample.sh

Experiments on UAI Data

Data can be collected from link. To run the simulations, follow the same procedure for experiments on Ising

Citation

Please cite our paper as:

@inproceedings{sansone2022lsb,
	title = {{LSB}: Local Self-Balancing {MCMC} in Discrete Spaces},
	author = {Sansone, Emanuele},
	booktitle = {Proceedings of the 39th International Conference on Machine Learning},
	pages = {19205--19220},
	year = {2022},
}