Streaming Inference for Infinite Feature Models

Authors: Rylan Schaeffer, Yilun Du, Gabrielle Kaili-May Liu, Ila Rani Fiete

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This code corresponds to our ICML 2022 publication Streaming Inference for Infinite Feature Models.

Setup

After cloning the repository, create a virtual environment for Python 3:

python3 -m venv ribp_venv

Then activate the virtual environment:

source ribp_venv/bin/activate

Ensure pip is up to date:

pip install --upgrade pip

Then install the required packages:

pip install -r requirements.txt

We did not test Python2, but Python2 may work.

Running

Each experiment has its own directory, each containing a main.py that creates a plots subdirectory (e.g. exp_00_ibp_prior/plots) and then reproduces the plots in the paper. Each main.py should be run from the repository directory e.g.:

python3 exp_00_ibp_prior/main.py

TODO

• MNIST or Omniglot
  • Implement MNIST comparison of classes based on inferred features
  • PCA MNIST
  • Implement Omniglot
• Landmark Dataset
  • linear gaussian -> NMF
• Derive
  • Factor Analysis
  • Non-negative Matrix Factorization
• Implement
  • Factor Analysis
  • Non-negative Matrix Factorization
• Implement IBP baselines
  • One of Paisley & Carin's papers
  • Particle Filtering (Wood and Griffiths)
• Refactor code into python package

Contact

Questions? Comments? Interested in collaborating? Open an issue or email Rylan Schaeffer (rylanschaeffer@gmail.com) and cc Yilun Du (yilundu@gmail.com) and Ila Fiete (fiete@mit.edu).