The package is the implementation of the bandit sampling algorithm for online variance reduction presented in the paper:
Online Variance Reduction for Stochastic Optimization Zalán Borsos, Andreas Krause, Kfir Y. Levy. Conference On Learning Theory (COLT), 2018.
The implementation is compatible with Python 2 and 3
First, install the dependencies with
pip install numpy nose Cython
You can install the package vrb locally by running:
python setup.py build_ext --inplace
The main entry point of the sampler is vrb.VarianceReducerBandit. The sampler should be used with alternatingly calling its sample() and update() as the following snippet shows:
n = 100 # number of data points
sampler = vrb.VarianceReducerBandit(n=n, random_state=0, reg=1, theta=0.1)
for t in range(100): # proceed in 100 rounds
i, p = sampler.sample(1) # sample 1 points
loss = adversary.get_loss(i, p) # loss provided by the adversary, e.g. norm of the gradient in SGD
sampler.update(loss) # feed the loss back to the sampler For a detailed example, see the ipython notebook in examples.
Use nose in the package directory to run the unit tests:
nosetests
Please send any feedback to Zalán Borsos.
The code is licenced under the MIT license and free to use by anyone without any restrictions.