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.
The documentation for this toolbox can be found here.
Note that to compile the cython files in casimir/data/named_entity_recognition, run
`./scripts/compile_cython.sh`
Feel free to submit a feature request, or better still, a pull request.
This project is licensed under the GPLv3 License - see the LICENSE.md file for details
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 = {NeurIPS},
year = {2018},
@incollection{pillutla-etal:casimir:ssp2023,
title = {{Modified Gauss-Newton Algorithms under Noise}},
author = {Pillutla, Krishna and
Roulet, Vincent and
Kakade, Sham M. and
Harchaoui, Zaid},
booktitle = {IEEE SSP},
year = {2023},
}
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.