关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

This is a collection of papers and other resources related to fairness.

A library for efficient similarity search and clustering of dense vectors.

Code for the SIGIR20 paper -- Measuring and Mitigating Item Under-Recommendation Bias inPersonalized Ranking Systems

视频的关键帧抓取，文字图像的矫正，图像拼接。

Experiments codes for RecSys '21 paper "Mitigating Confounding Bias in Recommendation via Information Bottleneck"

Code for the AAAI 2021 paper "Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation"

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

Papers and online resources related to machine learning fairness

Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

Easy access to myPersonality data

Paper List for Fair Graph Learning (FairGL).

Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks

A tool for single-cell data analysis.

final year project

Obtaining Dyadic Fairness by Optimal Transport

Easy access to myPersonality data

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Open source code for paper "EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks".

Analyzing and reducing bias in word-level language models

A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)

This repository contains implementations and illustrative code to accompany DeepMind publications

Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x

An Efficient and Unified Benchmark for GNN-based Recommendation.

This is an implementation for our SIGIR 2021 paper "Causal Intervention for Leveraging Popularity Bias inRecommendation" based on tensorflow..

A toolkit for developing and comparing reinforcement learning algorithms.