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Stein Density Ratio Estimation (SDRE) and Its Applications

Reference:

Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen, Fisher Efficient Inference of Intractable Models, E-print: arXiv:1805.07454, To appear NeurIPS2019, 2019,

Slides

Dependency on the kernel-gof package

If you want to run the neural network demo, you need to install kernel-gof package.

Install the sdre package

If you plan to modify our code (very likely, you will want to do so), it is best to install by:

  1. Clone this repository

  2. cd to the folder that you get, and install our package by (notice the dot at the end)

     pip install -e .
    

There an alternative way to install without cloning. But we do not recommend at this point since the code requires direct modification at this point.

Once installed, you should be able to do import sdre in a Python shell without any error.

To run test Stein density ratio estimation:

python script/DRE/demo.py
0
1
2
0 delta: [-0.00056058  0.00018254 -0.00041193]
100 delta: [-0.05309547  0.01852391 -0.03606012]
200 delta: [-0.09931757  0.03469031 -0.06197491]
...

demo.png

Estimating density ratio parameters using sdre.estimators.primal

delta_pri = primal(logpBar, f, XData, eta = .001, max_iter=50000)

Estimating density ratio parameters using of sdre.estimators.dual

delta_dua, dual_theta, LL, TfXData = dual(logpBar, f, XData)

To reproduce our MNIST experiments, please run

python sdre/trainnn.py
python examples/nn_MNIST/demo_NN.py
python examples/nn_MNIST/demo_NN_KSD.py
python examples/nn_MNIST/demo_NN_NCE.py

Then, in MATLAB, run

examples/nn_MNIST/plotMNIST

You should see mnist.png

Folder Structure:

  • sdre: the provided Python package.
  • script/DRE: Stein Density Ratio Estimation
  • script/Inference: Model Inference using SDRE
  • README: this file

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