Skip to content

Latest commit

 

History

History
62 lines (47 loc) · 3.45 KB

README.md

File metadata and controls

62 lines (47 loc) · 3.45 KB
Raw

Universalizing Weak Supervision

framework

This is the source code for our ICLR 2022 paper: Universalizing Weak Supervision by Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, and Frederic Sala. We propose a universal technique that enables weak supervision over any label type while still offering desirable properties, including practicalflexibility, computational efficiency, and theoretical guarantees.

System Requirements

  • Anaconda
  • Python 3.6
  • Pytorch
  • See environment.yml for details

Environment Setup

We recommend you create a conda environment as follows

conda env create -f environment.yml

and activate it with

conda activate uws

Running Experiments

  • Full ranking, partial ranking experiment
    • notebooks/{boardgames, movies}/RankingExperiments.ipynb (boardgames, movies)
      • To play with configurations, you may look into configs {board-games, imdb-tmdb}_ranking_experiment.yaml
      • Mainly changed configurations are
        • n_train
        • n_test
        • p: null | 0.2 | 0.4 | 0.6 | 0.8 (observational probability)
        • num_LFs: 3 | 6 | 9 | 12
        • inference_rule: weighted kemeny # | snorkel | kemeny | pairwise_majority | weighted_pairwise_majority
          • Note that snorkel is our baseline. kemeny and pariwise_majority is a majority voting for full rankings, and partial rankings respectively.
    • notebooks/synthetic/Full-Rankings-Experiments-Center-Recovery.ipynb (link)
    • notebooks/synthetic/Partial-Rankings-Experiments-Center-Recovery.ipynb (link)
  • Regression experiment
    • notebooks/{boardgames, movies}/RegressionExperiments.ipynb (boardgames) (movies)
    • notebooks/Regression-Experiments.ipynb (link)
  • Geodesic regression experiment
    • notebooks/geodesic-regression/geodesic_regression.ipynb (link)
  • Generic metric space experiment
    • notebooks/metric-spaces/generic_metric_spaces.ipynb (link)

Citation

If you find our repository useful for your research, please consider citing our paper:

@inproceedings{shin2022universalizing,
  title={Universalizing Weak Supervision},
  author={Shin, Changho and Li, Winfred and Vishwakarma, Harit and Roberts, Nicholas Carl and Sala, Frederic},
  booktitle={The Tenth International Conference on Learning Representations},
  year={2022}
}