Deep AUC Maximization

This is the official implementation of the paper "Stochastic AUC Maximization with Deep Neural Networks" published on ICLR2020.

Installation

Python=3.5
Numpy=1.18.5 
Scipy=1.2.1
Scikit-Learn=0.20.3
Pillow=5.0.0
Tensorflow>=1.10.0

Run

python PPD_SG.py/PPD_AdaGrad.py --dataset=10 --train_batch_size=128 --use_L2=False --split_index=4 --lr=0.01 --keep_index=0.1 --t0=200

Hyperparameter tuning

gamma=[500, 1000, 2000, ...]
eta = [0.1, 0.01, ...]
T0=[1000, 2000, 3000, ...,]

Bibtex

If you use this repository in your work, please cite our paper:

@inproceedings{
Liu2020Stochastic,
title={Stochastic AUC Maximization with Deep Neural Networks},
author={Mingrui Liu and Zhuoning Yuan and Yiming Ying and Tianbao Yang},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=HJepXaVYDr}
}