muvi 0.1.1

MuVI: A multi-view latent variable model with domain-informed structured sparsity for integrating noisy feature sets.

MuVI

A multi-view latent variable model with domain-informed structured sparsity, that integrates noisy domain expertise in terms of feature sets.

Quick links

Examples | Paper | BibTeX

Setup

We suggest using conda to manage your environments, and either pip or poetry to install muvi as a python package. Follow these steps to get muvi up and running!

Remotely

1. Create a python environment in conda:

conda create -n muvi python=3.9

2. Activate freshly created environment:

source activate muvi

3. Install muvi with pip:

python3 -m pip install git+https://github.com/MLO-lab/MuVI.git

Locally

1. Clone repository:

git clone https://github.com/MLO-lab/MuVI.git

2. Create a python environment in conda:

conda create -n muvi python=3.9

3. Activate freshly created environment:

source activate muvi

4. Install muvi with poetry:

cd MuVI
poetry install

Getting started

Check out basic tutorial to get familiar with MuVI!

Citation

If you use MuVI in your work, please use this BibTeX entry:

Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity

Arber Qoku and Florian Buettner

International Conference on Artificial Intelligence and Statistics (AISTATS) 2023

https://proceedings.mlr.press/v206/qoku23a.html