Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

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Deep Learning FATE
Overview

This repository holds the implementation for paper Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

Download our preprocessed UCI datasets to the data folder (created in the root path) via the following link

https://drive.google.com/drive/folders/1MlP5MiGeGNjb9GpWbI3HlUrpCFw2XqVA?usp=sharing

For Criteo and Avazu datasets, please download them from the Kaggle website.

To run the code, please refer to the bash script in each folder.

More information will be updated.

If you use the code or preprocessed datasets, please cite our paper:

@inproceedings{wu2021fate,
title = {Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach},
author = {Qitian Wu and Chenxiao Yang and Junchi Yan},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2021}
}
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Comments
  • About the CTR dataset preprocessing

    About the CTR dataset preprocessing

    Thanks for your outstanding paper! I would like to ask how to preprocess the ctr dataset? Is it convenient for you to provide data_filter4.pkl that appears in the code?

    opened by downeykking 1
  • About avazu and criteo files

    About avazu and criteo files

    Hello, I downloaded the preprocessed avazu and criteo files according to the address you gave. How are these two files processed? The preprocessing.py file given seems to be incomplete.

    opened by su-go 2
Owner
Qitian Wu
Qitian Wu
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