Users

A Selection of Users

Our library has gained global recognition among AI researchers, who adopt it into their research or projects. In the following sections, we proudly present a selection of users who have successfully leveraged our library to advance their work.

Publications Leveraging our Library

  1. 1. Nguyen, Ngoc Dang, et al. "AUC maximization for low-resource named entity recognition." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 11. 2023.
  2. 2. Dai, Siran, et al. "DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework." Advances in Neural Information Processing Systems 36 (2024).
  3. 3. Xu, Jiacen, Xiaokui Shu, and Zhou Li. "Understanding and Bridging the Gap Between Unsupervised Network Representation Learning and Security Analytics." 2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 2023.
  4. 4. Dong, Yingjun, et al. "A self-supervised learning approach for registration agnostic imaging models with 3D brain CTA." Iscience (2024).
  5. 5. Xu, Shoukun, et al. "FAUC-S: Deep AUC maximization by focusing on hard samples." Neurocomputing 571 (2024): 127172.
  6. 6. Gao, Peifeng, et al. "Towards Decision-Friendly AUC: Learning Multi-Classifier with AUCµ." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 6. 2023.
  7. 7. Zhang, Chenkang, et al. "Doubly robust AUC optimization against noisy and adversarial samples." Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023.
  8. 8. Xiong, Ziran, Wanli Shi, and Bin Gu. "End-to-End Semi-Supervised Ordinal Regression AUC Maximization with Convolutional Kernel Networks." Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022.
  9. 9. Vogelbaum, Evan, Logan Engstrom, and Aleksander Madry. "What Works in Chest X-Ray Classification? A Case Study of Design Choices." ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH). 2023.
  10. 10. Wu, Xidong, Feihu Huang, and Heng Huang. "Fast stochastic recursive momentum methods for imbalanced data mining." 2022 IEEE International Conference on Data Mining (ICDM). IEEE, 2022.
  11. 11. Chen, Yanshuo, et al. "Robust and Accurate Doublet Detection of Single-Cell Sequencing Data via Maximizing Area Under Precision-Recall Curve." bioRxiv (2023): 2023-10.
  12. 12. Shao, Huiyang, et al. "Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm." Advances in Neural Information Processing Systems 35 (2022): 38667-38679.
  13. 13. Shi, Wentao, et al. "On the Theories Behind Hard Negative Sampling for Recommendation." Proceedings of the ACM Web Conference 2023. 2023.
  14. 14. Jiang, Chenzhi, et al. "A Momentum Loss Reweighting Method for Improving Recall." Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023.
  15. 15. Pachetti, Eva, Sotirios A. Tsaftaris, and Sara Colantonio. "Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification." arXiv preprint arXiv:2403.17530 (2024).

Projects Leveraging our Library

  1. 1. The fist place solution on the ogbg-molhiv leaderboard, https://github.com/zhangxwww/HyperFusion
  2. 2. The second place solution on the ogbg-molhiv leaderboard, https://github.com/LARS-research/PAS-OGB
  3. 3. The third place solution on the ogbg-molhiv leaderboard, https://github.com/TencentYoutuResearch/HIG-GraphClassification