BayesOpt-LV

Optimizing Value-at-Risk and Conditional Value-at-Risk of Black Box Functions with Lacing Values (LV)

About

This repository contains the source code for

ICML'21 paper: Value-at-Risk Optimization with Gaussian Processes
NeurIPS'21 paper: Optimizing Conditional Value-At-Risk of Black-Box Functions

Requirements

numpy
scipy
tensorflow 1.14.0
tensorflow-probability 0.7.0
gpflow 1.5.1

Instructions

The examples of running scripts are in running_scripts folder. The optimization results are stored in a folder named with the objective function in running_scripts. Objective functions are found in functions.py.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
portfolio_data		portfolio_data
running_scripts		running_scripts
yacht_data		yacht_data
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
approximator.py		approximator.py
cvaropt_discrete.py		cvaropt_discrete.py
functions.py		functions.py
optfunc.py		optfunc.py
run_cvar_TS_discrete.py		run_cvar_TS_discrete.py
run_cvar_UCB_discrete.py		run_cvar_UCB_discrete.py
run_var_UCB_continuous.py		run_var_UCB_continuous.py
run_var_UCB_discrete.py		run_var_UCB_discrete.py
utils.py		utils.py
varopt.py		varopt.py
varopt_discrete.py		varopt_discrete.py
visualize_cvar_func.py		visualize_cvar_func.py
visualize_var_func.py		visualize_var_func.py

License

qphong/BayesOpt-LV

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