Python package to estimate sensitivity of optima to hyperparameters.
# “Variational inference tools to leverage estimator sensitivity”: vittles.
This is a library (very much still in development) intended to make sensitivity analysis easier for optimization problems. For background and motivation, see the following papers:
A Higher-Order Swiss Army Infinitesimal Jackknife Ryan Giordano, Michael I. Jordan, Tamara Broderick <https://arxiv.org/abs/1907.12116>
Covariances, Robustness, and Variational Bayes Ryan Giordano, Tamara Broderick, Michael I. Jordan <https://arxiv.org/abs/1709.02536>
A Swiss Army Infinitesimal Jackknife Ryan Giordano, Will Stephenson, Runjing Liu, Michael I. Jordan, Tamara Broderick <https://arxiv.org/abs/1806.00550>
Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics Runjing Liu, Ryan Giordano, Michael I. Jordan, Tamara Broderick <https://arxiv.org/abs/1810.06587>
## Using the package.
We welcome new users! However, please be aware that the package is still in development. We encourage users to contact the author (github user rgiordan) for advice, bugs, or if you’re using the package for something important.
To install the latest tagged version, install with pip:
python3 -m pip install vittles.
Note that vittles is under rapid development, so you may want to clone the respository and use the master branch instead.
### Documentation and Examples.
For examples and API documentation, see [readthedocs](https://vittles-python.readthedocs.io/en/latest/index.html).
Alternatively, check out the repo and run make html in docs/.
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