Introduction

This is the Pytorch implementation of our paper: A Context-Integrated Transformer-Based Neural Network for Auction Design (https://arxiv.org/abs/2201.12489) in ICML 2022.

Requirements

• Python >= 3.6
• Pytorch 1.10.0
• Argparse
• Logging
• Tqdm
• Scipy

Usage

Generate the data

cd data_gen
# For Setting G,H,I
python data_gen_continous.py

# For Setting D,E,F
python data_gen_discrete.py

Train CITransNet

cd CITransNet
# For Setting G
python main_2x5_c.py

# For Setting H
python main_3x10_c.py

# For Setting I
python main_5x10_c.py

# For Setting D
python main_2x5_d.py

# For Setting E
python main_3x10_d.py

# For Setting F, it is recommended to train it with 2 GPUs.
CUDA_VISIBLE_DEVICES=0,1 python main_5x10_d.py --data_parallel True

Acknowledgement

Our code is built upon the implementation of https://arxiv.org/abs/2003.01497.