This GitHub repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.'

Overview

About Repository

This repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.'

About Code

Dependencies:

Python version: 3.9.1
Packages: numpy, pandas, scipy, matplotlib, and tqdm

Description of Files:

1. figure1a.py: This file contains code used for generating the 'Figure 1 (a)' in the paper.
2. figure1b.py: This file contains code used for generating the 'Figure 1 (b)' in the paper.
3. figure1c.py: This file contains code used for generating the 'Figure 1 (c)' in the paper.
4. figure2a.py: This file contains code used for generating the 'Figure 2 (a)' in the paper.
5. figure2b.py: This file contains code used for generating the 'Figure 2 (b)' in the paper.
6. figure2c.py: This file contains code used for generating the 'Figure 2 (c)' in the paper.
7. figure3a.py: This file contains code used for generating the 'Figure 3 (a)' in the paper.
8. figure3b.py: This file contains code used for generating the 'Figure 3 (b)' in the paper.
9. figure3c.py: This file contains code used for generating the 'Figure 3 (c)' in the paper.

Directories:

1. plots: This directory contains plots generated after running different python files. 

How to Run?

To generate any figure used in the code, run 'python file_name.py' where file_name corresponds to the python file of figure. 
Ensures all dependencies met before running the code.
Owner
Arun Verma
Research Fellow, National University of Singapore
Arun Verma
An intuitive library to extract features from time series

Time Series Feature Extraction Library Intuitive time series feature extraction This repository hosts the TSFEL - Time Series Feature Extraction Libra

Associação Fraunhofer Portugal Research 589 Jan 04, 2023
Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.

Optimum Transformers Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime. Installatio

Aleksey Korshuk 115 Dec 16, 2022
Benchmark for Answering Existential First Order Queries with Single Free Variable

EFO-1-QA Benchmark for First Order Query Estimation on Knowledge Graphs This repository contains an entire pipeline for the EFO-1-QA benchmark. EFO-1

HKUST-KnowComp 14 Oct 24, 2022
Miscellaneous and lightweight network tools

Network Tools Collection of miscellaneous and lightweight network tools to simplify daily operations, administration, and troubleshooting of networks.

Nicholas Russo 22 Mar 22, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression"

Polarized Self-Attention: Towards High-quality Pixel-wise Regression This is an official implementation of: Huajun Liu, Fuqiang Liu, Xinyi Fan and Don

DeLightCMU 212 Jan 08, 2023
Keep CALM and Improve Visual Feature Attribution

Keep CALM and Improve Visual Feature Attribution Jae Myung Kim1*, Junsuk Choe1*, Zeynep Akata2, Seong Joon Oh1† * Equal contribution † Corresponding a

NAVER AI 90 Dec 07, 2022
Implementation of the method described in the Speech Resynthesis from Discrete Disentangled Self-Supervised Representations.

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations Implementation of the method described in the Speech Resynthesis from Di

4 Mar 11, 2022
This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures

Introduction This Repo is the official CUDA implementation of ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures. @inproceedings{Wa

Jiaqi Wang 42 Jan 07, 2023
Xintao 1.4k Dec 25, 2022
PyTorch implementation of SIFT descriptor

This is an differentiable pytorch implementation of SIFT patch descriptor. It is very slow for describing one patch, but quite fast for batch. It can

Dmytro Mishkin 150 Dec 24, 2022
《Fst Lerning of Temporl Action Proposl vi Dense Boundry Genertor》(AAAI 2020)

Update 2020.03.13: Release tensorflow-version and pytorch-version DBG complete code. 2019.11.12: Release tensorflow-version DBG inference code. 2019.1

Tencent 338 Dec 16, 2022
SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers

SAGE: Sensitivity-guided Adaptive Learning Rate for Transformers This repo contains our codes for the paper "No Parameters Left Behind: Sensitivity Gu

Chen Liang 23 Nov 07, 2022
Visual Tracking by TridenAlign and Context Embedding

Visual Tracking by TridentAlign and Context Embedding (TACT) Test code for "Visual Tracking by TridentAlign and Context Embedding" Janghoon Choi, Juns

Janghoon Choi 32 Aug 25, 2021
This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationships.

Auto-Lambda This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationship

Shikun Liu 76 Dec 20, 2022
PAthological QUpath Obsession - QuPath and Python conversations

PAQUO: PAthological QUpath Obsession Welcome to paquo 👋 , a library for interacting with QuPath from Python. paquo's goal is to provide a pythonic in

Bayer AG 60 Dec 31, 2022
A knowledge base construction engine for richly formatted data

Fonduer is a Python package and framework for building knowledge base construction (KBC) applications from richly formatted data. Note that Fonduer is

HazyResearch 386 Dec 05, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
Tutorial for the PERFECTING FACTORY 5.0 WITH EDGE-POWERED AI workshop

Workshop Advantech Jetson Nano This tutorial has been designed for the PERFECTING FACTORY 5.0 WITH EDGE-POWERED AI workshop in collaboration with Adva

Edge Impulse 18 Nov 22, 2022