This is the source code for "Boosting Black Box Variational Inference," by Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch. https://arxiv.org/abs/1806.02185.
@article{locatello2018boosting,
title={Boosting Black Box Variational Inference},
author={Locatello, Francesco and Dresdner, Gideon and Khanna, Rajiv and Valera, Isabel and R{\"a}tsch, Gunnar},
journal={arXiv preprint arXiv:1806.02185},
year={2018}
}
- Setup the dependencies using conda:
conda env create -n bbbvi --file conda-env.txt
- Activate the environment
source activate bbbvi
- Install the package for development
python setup.py develop
To recreate the Bayesian Linear Regression results in Table 1 of the paper, run
python blr.py \
--base_dist lpl \
--exp $EXPERIMENT \
--outdir $OUTDIR \
--seed $seed"
Where $EXPERIMENT is either chem or wine.
To recreate the Bayesian matrix factorization results in Table 1 of the paper, run
python matrix_factorization.py --D $D --outdir $OUTDIR --seed $seed
To recreate Figure 1 of the experiment run
python mixture_model_relbo.py \
--relbo_reg 1.0 \
--relbo_anneal linear \
--exp mixture \
--fw_variant $variant \
--outdir $OUTDIR"
Where $variant is fixed, line_search, or fc.
This project is licensed under the MIT License - see the LICENSE file for details.