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Source code for Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective at NeurIPS 2020

This uses Sacred's command line interface. To see Sacred's options run

python run.py --help

To see tunable hyperparameters

python run.py print_config

which can be set using with:

python main.py with learning_task='continuous_vae' loss=tvo schedule='gp_bandit' S=50 K=5 epochs=1000

To save data to the filesystem, add a Sacred FileStorageObserver

python main.py with learning_task='continuous_vae' loss=tvo schedule='gp_bandit' S=50 K=5 epochs=1000 -F ./runs

The TVO loss is computed in get_thermo_loss_from_log_weight_log_p_log_q in losses.py. This function is identical to the one found in the discrete_vae directory.

The main training loop is in train in run.py.

This package is inspired and extended from https://github.com/vmasrani/tvo

Reference

Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood.  "Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective." Advances in Neural Information Processing Systems (NeurIPS), 2020.

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Code for the "Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective" at NeurIPS20

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