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Codes for "Understanding MCMC Dynamics as Flows on the Wasserstein Space" (ICML-19)
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lda_sett_icml
README.md
bnn_tq_def.py
bnn_tq_run.py
dynamics.py
hyper_dynamics.py
lda_build.py
lda_def.py
lda_run.py
lda_sample_z_ids.pyx
synth_run.ipynb

README.md

Understanding MCMC Dynamics as Flows on the Wasserstein Space

Chang Liu, Jingwei Zhuo, and Jun Zhu

Instructions

  • For the synthetic experiment: Directly open "synth_run.ipynb" in a jupyter notebook.

  • For the Latent Dirichlet Allocation experiment: First run

      "python lda_build.py build_ext --inplace"
    

    to compile the Cython code, then run

      "python lda_run.py ./lda_sett_icml/[a specific settings file]"
    

    to conduct experiment under the specified settings. The ICML dataset can be downloaded from

      https://cse.buffalo.edu/ ̃changyou/code/SGNHT.zip
    

    Codes are developed based on the codes of "Stochastic Gradient Riemannian Langevin Dynamics for Latent Dirichlet Allocation" (Patterson and Teh, 2013).

  • For the Bayesian neural network experiment: Directly edit the file "bnn_tq_run.py" to make a setting, and run

      "python bnn_tq_run.py"
    

    to conduct experiment under the specified settings. Experiment setup follows the one of "Stochastic Gradient Hamiltonian Monte Carlo" (Chen et al., 2014)

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