citations.bib

@inproceedings{yuan2023libauc,
  title={LibAUC: A Deep Learning Library for X-Risk Optimization},
  author={Zhuoning Yuan and Dixian Zhu and Zi-Hao Qiu and Gang Li and Xuanhui Wang and Tianbao Yang},
  booktitle={29th SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2023}
}
  
@article{yang2022algorithmic,
  title={Algorithmic Foundation of Deep X-Risk Optimization},
  author={Yang, Tianbao},
  journal={arXiv preprint arXiv:2206.00439},
  year={2022}
}

@article{yang2022auc,
  title={AUC Maximization in the Era of Big Data and AI: A Survey},
  author={Yang, Tianbao and Ying, Yiming},
  journal={arXiv preprint arXiv:2203.15046},
  year={2022}
}

@article{yuan2022provable,
  title={Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance},
  author={Yuan, Zhuoning and Wu, Yuexin and Qiu, Zihao and Du, Xianzhi and Zhang, Lijun and Zhou, Denny and Yang, Tianbao},
  booktitle={International Conference on Machine Learning},
  year={2022},
  organization={PMLR}
}


@article{qiu2022large,
  title={Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence},
  author={Qiu, Zi-Hao and Hu, Quanqi and Zhong, Yongjian and Zhang, Lijun and Yang, Tianbao},
  booktitle={International Conference on Machine Learning},
  year={2022},
  organization={PMLR}
}

@article{zhu2022auc,
  title={When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee},
  author={Zhu, Dixian and Li, Gang and Wang, Bokun and Wu, Xiaodong and Yang, Tianbao},
  booktitle={International Conference on Machine Learning},
  year={2022},
  organization={PMLR}
}


@inproceedings{yuan2021compositional,
  title={Compositional Training for End-to-End Deep AUC Maximization},
  author={Yuan, Zhuoning and Guo, Zhishuai and Chawla, Nitesh and Yang, Tianbao},
  booktitle={International Conference on Learning Representations},
  year={2022},
  organization={PMLR}
}


@inproceedings{yuan2021federated,
  title={Federated deep AUC maximization for hetergeneous data with a constant communication complexity},
  author={Yuan, Zhuoning and Guo, Zhishuai and Xu, Yi and Ying, Yiming and Yang, Tianbao},
  booktitle={International Conference on Machine Learning},
  pages={12219--12229},
  year={2021},
  organization={PMLR}
}

@inproceedings{yuan2021large,
  title={Large-scale robust deep auc maximization: A new surrogate loss and empirical studies on medical image classification},
  author={Yuan, Zhuoning and Yan, Yan and Sonka, Milan and Yang, Tianbao},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3040--3049},
  year={2021}
}
@article{qi2021stochastic,
  title={Stochastic optimization of areas under precision-recall curves with provable convergence},
  author={Qi, Qi and Luo, Youzhi and Xu, Zhao and Ji, Shuiwang and Yang, Tianbao},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={1752--1765},
  year={2021}
}