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

The official code of "SCROLLS: Standardized CompaRison Over Long Language Sequences".

shufflev2-yolov5：lighter, faster and easier to deploy

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Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

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[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Learning Features with Parameter-Free Layers (ICLR 2022)

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue

StarGAN - Official PyTorch Implementation (CVPR 2018)

Code accompanying the paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (Chen et al., CVPR 2020, Oral).

Repository for benchmarking graph neural networks

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

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