Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)

Related tags

Deep Learning PU_learning

Overview

Mixture Proportion Estimation and PU Learning: A Modern Approach

This repository is the official implementation of Mixture Proportion Estimation and PU Learning: A Modern Approach. If you find this repository useful or use this code in your research, please cite the following paper:

Garg, S., Wu, Y., Smola, A., Balakrishnan, S., Lipton, Z. (2021). Mixture Proportion Estimation and PU Learning: A Modern Approach. arxiv preprint arXiv:2111.00980.

@article{garg2021mixture,
    title={Mixture Proportion Estimation and PU Learning: A Modern Approach},
    author={Garg, Saurabh and Wu, Yifan and Smola, Alex and Balakrishnan, Sivaraman and Lipton, Zachary C.},
    year={2021},
    journal={arXiv preprint arXiv:2111.00980},
}

Requirements

To install requirements, setup a conda enviornment using the following command:

conda env create --name PU_learning python=3.7 --file PU_env

Experiments

Working in progress! More details soon.

License

This repository is licensed under the terms of the MIT non-commercial License.

Questions?

For more details, refer to the accompanying NeurIPS 2021 paper (Spotlight): Mixture Proportion Estimation and PU Learning: A Modern Approach. If you have questions, please feel free to reach us at [email protected] or open an issue.

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Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)

Related tags

Overview

Mixture Proportion Estimation and PU Learning: A Modern Approach

Requirements

Experiments

License

Questions?

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