Semi-Modular Inference

This repo contains code for our AISTATS article on Semi-Modular Inference.

Semi-Modular Inference (SMI) is a modification of Bayesian inference in multi-modular settings, which enables tunable and directed flow of information between modules.

For an introduction to SMI, we invite you to watch our slideslive presentation (best on 1.5x),

Citation

If you find Semi-Modular Inference relevant for your scientific publication, we encourage you to add the following reference:

@InProceedings{Carmona2020smi,
  title = {Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components},
  author = {Carmona, Chris U. and Nicholls, Geoff K.},
  booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020},
  year = {2020},
  editor = {Silvia Chiappa and Roberto Calandra},
  volume = {108},
  pages = {4226--4235},
  series = {Proceedings of Machine Learning Research},
  month = {26--28 Aug},
  publisher = {PMLR},
  pdf = {http://proceedings.mlr.press/v108/carmona20a/carmona20a.pdf},
  url = {http://proceedings.mlr.press/v108/carmona20a.html},
  arxivId = {2003.06804},
}

Installation

You can install the devel version of aistats2020smi from our github repository

#install.packages("devtools")
devtools::install_github("christianu7/aistats2020smi")

Reproducibility

The main article and supplementary material can be reproduced entirely using a .Rnw file included in this repo. Executing the following command will generate a pdf file in your current directory:

print( getwd() )
aistats2020smi::generate_article( out_dir=getwd() )

If you prefer to keep and analyse intermediate outputs, consider executing the following commands:

path = "~/smi_article"
dir.create(path)
aistats2020smi::download_mcmc_results( mcmc_dir = path )
aistats2020smi::generate_article( out_dir = path, mcmc_dir = path )

Coming soon, Variational SMI

You may also be interested in our current work on Scalable Semi-Modular Inference via Normalizing flows. Here is a teaser of our current work