Smoothing and acceleration for max-margin structured prediction
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casimir
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README.md

Casimir: Catalyst, smoothing, and inference

A toolbox of selected optimization algorithms including Casimir-SVRG (as well as the special cases of Catalyst-SVRG and SVRG) for unstructured tasks such as binary classification, and structured prediction tasks such as object localization or named entity recognition. This is code accompanying the paper "A Smoother Way to Train Structured Prediction Models" in NeurIPS 2018.

Documentation

The documentation for this toolbox can be found here.

Contributing

Feel free to submit a feature request, or better still, a pull request.

Authors

License

This project is licensed under the GPLv3 License - see the LICENSE.md file for details

Cite

If you found this package useful, please cite the following work.

@incollection{pillutla-etal:casimir:neurips2018,
title = {A {S}moother {W}ay to {T}rain {S}tructured {P}rediction {M}odels},
author = {Pillutla, Krishna and
          Roulet, Vincent and 
          Kakade, Sham M. and
          Harchaoui, Zaid},
booktitle = {Advances in Neural Information Processing Systems 31},
year = {2018},
}

Acknowledgments

This work was supported by NSF Award CCF-1740551, the Washington Research Foundation for innovation in Data-intensive Discovery, and the program “Learning in Machines and Brains” of CIFAR.