MIDAM

The official implementation of 'Provable Multi-instance Deep AUC Maximization with Stochastic Pooling', ICML2023

Here are some dependencies (with the version for the experiments results reported in the paper): torch==1.9.0, numpy==1.17.4, CUDA version:12.0 on NVIDIA Quadro RTX 6000 card.

This is the code that runs MIDAM with stochastic softmax or attention pooling on benchmark datasets, such as MUSK1&2, Fox, Tiger, Elephant, Breast Cancer, etc.

Examples:

Run MIDAM with stochastic attention pooling on Fox data

CUDA_VISIBLE_DEVICES=0 python3 experiment.py --dataset=Fox  --loss=MIDAM-att  --momentum=0.1  --seed=123 --lr=1e-2 --bag_batch_size=4

Run MIDAM with stochastic softmax pooling on Fox data

CUDA_VISIBLE_DEVICES=0 python3 experiment.py --dataset=Fox  --loss=MIDAM-smx  --momentum=0.1  --seed=123 --lr=1e-2 --bag_batch_size=4

Others:

Please make sure you have the data on the data folder (some smaller datasets are already included). Please refer to the experiment.py and datasets.py for how to load the data.

Citation:

@inproceedings{zhu2023provable,
	title={Provable Multi-instance Deep AUC Maximization with Stochastic Pooling},
	author={Dixian Zhu and Bokun Wang and Zhi Chen and Yaxing Wang and Milan Sonka and Xiaodong Wu and Tianbao Yang},
	booktitle={Proceedings of the 40th International Conference on Machine Learning},
	year={2023}
	}