Learning kernels to maximize the power of MMD tests

Related tags

Deep Learning python machine-learning theano tensorflow generative-adversarial-network statistical-tests kernel-methods hypothesis-testing maximum-mean-discrepancy shogun
Overview

Code for the paper "Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy" (arXiv:1611.04488; published at ICLR 2017), by Dougal J. Sutherland (@dougalsutherland), Hsiao-Yu Tung, Heiko Strathmann (@karlnapf), Soumyajit De (@lambday), Aaditya Ramdas, Alex Smola, and Arthur Gretton.

This code is under a BSD license, but if you use it, please cite the paper.

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Comments
  • tmmd not maximizing the test power

    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?

    opened by ylfzr 2
  • Python package tables is required but not automatically installed

    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

    opened by normanius 1
  • Missing coefficient for cross term in MMD

    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).

    https://github.com/djsutherland/opt-mmd/blob/5c02a92972df099628a4bc8351980ad9f317b6d0/two_sample/mmd.py#L44-L49

    Is this implementation intended to match the estimate linked above?

    Cheers

    opened by nik-sm 0
Owner
Danica J. Sutherland
Machine learning professor.
Danica J. Sutherland
GitHub https://arxiv.org/abs/1611.04488
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