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

Last update: Nov 11, 2021

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Deep Learning BayesOpt-LV

Overview

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.

Owner

GitHub Repository

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

Related tags

Overview

BayesOpt-LV

About

Requirements

Instructions

Owner

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

Instance-wise Occlusion and Depth Orders in Natural Scenes (CVPR 2022)

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Official implementation of EfficientPose

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A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

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A GridMixup augmentation, inspired by GridMask and CutMix

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Re-implementation of the Noise Contrastive Estimation algorithm for pyTorch, following "Noise-contrastive estimation: A new estimation principle for unnormalized statistical models." (Gutmann and Hyvarinen, AISTATS 2010)

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