This is the code repository for the paper "Identification of the Generalized Condorcet Winner in Multi-dueling Bandits" (NeurIPS 2021).

Overview

Code Repository for the Paper

"Identification of the Generalized Condorcet Winner in Multi-dueling Bandits"

   (To appear in: Proceedings of NeurIPS2021)

The code is written in Python 3.7.

You can cite our paper as follows:

@inproceedings{Haddenhorst2021,
  title={Identification of the Generalized Condorcet Winner in Multi-dueling Bandits},
  author={Haddenhorst, Bj{\"o}rn and Bengs, Viktor and H{\"u}llermeier, Eyke},
  booktitle = {Proceedings of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)},
  year={2021},
}

Requirements

To install requirements:

pip install -r requirements.txt

Evaluation

  • (A) To obtain the evaluation results of the algorithms, uncomment the corresponding code in "Neurips2021_experiments.py" and execute it.
  • (B) To repeat the empirical comparison of the two lower bounds (Prop. 4.1 and Thm 5.2) for the single bandit case (m=k), simply execute "NeurIPS_LB_comparison.py".

Results

  • After repeating all experiments in (A), the results shown in the tables are written saved the following files
TABLE(S) FILE
2 Experiment_PW_m5.txt
3,6,7 Experiment1_m5_gamma_005.txt
3,6,7 Experiment1_m10_gamma_005.txt
4 Experiment_PW_m10.txt
5 Experiment_PW_PWData.txt
6,7 Experiment1_m15_gamma_005.txt
6,7 Experiment1_m20_gamma_005.txt
8 Experiment_DKWT_vs_algo5_v1.txt
9 Experiment_DKWT_vs_algo5_v2.txt
  • The results for (B) are only shown in the terminal and not saved to any file.

In case of any questions, please contact Björn Haddenhorst ([email protected]).

Owner
Since 2019: PhD student of Prof. Eyke Hüllermeier at Paderborn University
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).

Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs This repository contains PyTorch implementation of our pa

Shizhe Chen 178 Dec 29, 2022
Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics

[AAAI2022] Detecting Human-Object Interactions with Object-Guided Cross-Modal Calibrated Semantics Overall pipeline of OCN. Paper Link: [arXiv] [AAAI

13 Nov 21, 2022
A Repository of Community-Driven Natural Instructions

A Repository of Community-Driven Natural Instructions TLDR; this repository maintains a community effort to create a large collection of tasks and the

AI2 244 Jan 04, 2023
This is the dataset and code release of the OpenRooms Dataset.

This is the dataset and code release of the OpenRooms Dataset.

Visual Intelligence Lab of UCSD 95 Jan 08, 2023
InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images

InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images Hong Wang, Yuexiang Li, Haimiao Zhang, Deyu Men

Hong Wang 4 Dec 27, 2022
ReAct: Out-of-distribution Detection With Rectified Activations

ReAct: Out-of-distribution Detection With Rectified Activations This is the source code for paper ReAct: Out-of-distribution Detection With Rectified

38 Dec 05, 2022
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction. NeurIPS 2021.

Gengshan Yang 59 Nov 25, 2022
The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

Autoregressive Image Generation using Residual Quantization (CVPR 2022) The official implementation of "Autoregressive Image Generation using Residual

Kakao Brain 529 Dec 30, 2022
A little Python application to auto tag your photos with the power of machine learning.

Tag Machine A little Python application to auto tag your photos with the power of machine learning. Report a bug or request a feature Table of Content

Florian Torres 14 Dec 21, 2022
Pytorch implementation for the paper: Contrastive Learning for Cold-start Recommendation

Contrastive Learning for Cold-start Recommendation This is our Pytorch implementation for the paper: Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan L

45 Dec 13, 2022
Contextual Attention Network: Transformer Meets U-Net

Contextual Attention Network: Transformer Meets U-Net Contexual attention network for medical image segmentation with state of the art results on skin

Reza Azad 67 Nov 28, 2022
Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

recommendationsystem matrixfactorization colloborative filtering recommendation

kunal jagdish madavi 1 Jan 01, 2022
This repository contains the code and models necessary to replicate the results of paper: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

OPTML Group 2 Oct 05, 2022
Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Rainbow 🌈 An implementation of Rainbow DQN which outperforms the paper's (Hessel et al. 2017) results on 40% of tested games while using 20x less dat

Dominik Schmidt 31 Dec 21, 2022
SlotRefine: A Fast Non-Autoregressive Model forJoint Intent Detection and Slot Filling

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article

Moore 34 Nov 03, 2022
A TensorFlow implementation of SOFA, the Simulator for OFfline LeArning and evaluation.

SOFA This repository is the implementation of SOFA, the Simulator for OFfline leArning and evaluation. Keeping Dataset Biases out of the Simulation: A

22 Nov 23, 2022
Graph Analysis From Scratch

Graph Analysis From Scratch Goal In this notebook we wanted to implement some functionalities to analyze a weighted graph only by using algorithms imp

Arturo Ghinassi 0 Sep 17, 2022
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020)

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give

59 Dec 09, 2022
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

Temporally Efficient Vision Transformer for Video Instance Segmentation Temporally Efficient Vision Transformer for Video Instance Segmentation (CVPR

Hust Visual Learning Team 203 Dec 31, 2022
🏅 The Most Comprehensive List of Kaggle Solutions and Ideas 🏅

🏅 Collection of Kaggle Solutions and Ideas 🏅

Farid Rashidi 2.3k Jan 08, 2023