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Codes for "Understanding and Accelerating Particle-Based Variational Inference" (ICML-19)
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README.md
blr_def.py
blr_run.ipynb
bnn_def.py
bnn_run.py
bnn_set_kin8nm.py
dynamics.py
lda_build.py
lda_def.py
lda_run.py
lda_sample_z_ids.pyx
lda_set_icml_po.py
lda_set_icml_wgd.py
lda_set_icml_window.py
lda_set_icml_wnag.py
lda_set_icml_wnes1.py
synthetic_run.ipynb

README.md

Understanding and Accelerating Particle-Based Variational Inference

Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, and Lawrence Carin

Instructions

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

  • For the Bayesian logistic regression experiment: Open "blr_run.ipynb" in a jupyter notebook to run trials and view results. Codes are developed based on the codes of "Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm" (Liu and Wang, 2016).

  • For the Bayesian neural network experiment: Edit the settings file "bnn_set_kin8nm.py" to choose a setting, and then run the command

      "python bnn_run.py bnn_set_kin8nm.py"
    

    to conduct experiment under the specified settings. Codes are developed based on the codes of "Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm" (Liu and Wang, 2016).

  • 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 [a settings file beginning with 'lda_set_icml_']"
    

    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).

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