/lio

Learning from Indirect Observations

Primary LanguageJupyter NotebookMIT LicenseMIT

LIO: Learning from Indirect Observations

A package for weakly supervised learning research based on PyTorch

license pypi

Installation

pip install lio

or

git clone https://github.com/YivanZhang/lio.git
pip install -e .

Most of the modules are designed as small (higher-order) functions.
Feel free to copy-paste only what you need for your existing workflow to reduce dependencies.

References

  • Approximating Instance-Dependent Noise via Instance-Confidence Embedding
    Yivan Zhang and Masashi Sugiyama
    [arXiv] [code]

  • Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
    Yivan Zhang, Gang Niu, and Masashi Sugiyama
    [arXiv] [ICML'21] [poster] [code]

  • Learning from Aggregate Observations
    Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, and Masashi Sugiyama
    [arXiv] [NeurIPS'20] [poster]

  • Learning from Indirect Observations
    Yivan Zhang, Nontawat Charoenphakdee, and Masashi Sugiyama
    [arXiv]