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Learning kernels to maximize the power of MMD tests

Home Page: https://arxiv.org/abs/1611.04488

License: BSD 3-Clause "New" or "Revised" License

Python 72.60% Jupyter Notebook 26.74% Shell 0.65%
machine-learning hypothesis-testing generative-adversarial-network maximum-mean-discrepancy shogun theano tensorflow python kernel-methods statistical-tests

opt-mmd's Issues

Missing coefficient for cross term in MMD

Apologies if I'm missing something obvious, but the estimate defined in equation (3) of https://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf looks slightly different to the MMD estimate implemented here.

The first term makes sense to me, and subtracting m accounts for the summation over i != j (since diagonal of RBF kernel will add to m). Likewise for second term.
However the third term seems to be missing a factor 1 / (m*n).

opt-mmd/two_sample/mmd.py

Lines 44 to 49 in 5c02a92

m = K_XX.shape[0]
n = K_YY.shape[0]
mmd2 = ((K_XX.sum() - m) / (m * (m - 1))
+ (K_YY.sum() - n) / (n * (n - 1))
- 2 * K_XY.mean())

Is this implementation intended to match the estimate linked above?

Cheers

tmmd not maximizing the test power

Hi,
Thanks for sharing. In the tmmd_model.py file, the optimizer seems just trys to minimize the ratio-loss, which should be the approximated test power mentioned in the paper. Shouldn't we increase the test power? I can't find any "-" there. Do I miss anything?

Python package tables is required but not automatically installed

Issue: python package tables is required by pandas, but not automatically installed.

How to reproduce:

python fixed_run.py -n 500 --blobs 1

Error message:

HDFStore requires PyTables, "No module named tables" problem importing

Fix:
pip install tables fixed the problem for me.
Add requirement tables to requirements.txt

System:
MacOS 10.11., python 2.7.12

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