Zi-Hao Qiu @ LAMDA

Zi-Hao Qiu (Z.-H. Qiu) Ph.D. Student LAMDA Group Department of Computer Science National Key Laboratory for Novel Software Technology Nanjing University Laboratory: 113, Building of Computer Science and Technology, Xianlin Campus of Nanjing University email: qiuzh@lamda.nju.edu.cn

Currently I am a first year Ph.D. student of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group(LAMDA Publications), led by professor Zhi-Hua Zhou.

Supervisor

Biography

• I received my B.Sc. degree from Nanjing University in June 2019.

• After that, I admitted to study for a M.Sc. degree in Nanjing University without entrance examination.

• From september 2021, I started my Ph.D. degree under the supervision of Professor Lijun Zhang.

Research Interest

Publications

• Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, and Tianbao Yang. To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. In Proceedings of the 41th International Conference on Machine Learning (ICML '24), to appear, 2024. [paper] [code]

• Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan, Denny Zhou, Lijun Zhang, and Tianbao Yang. Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. In Proceedings of the 40th International Conference on Machine Learning (ICML '23), pages 28389-28421, 2023. [paper] [code]

• Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, and Tianbao Yang. Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. In Proceedings of the 40th International Conference on Machine Learning (ICML '23), pages 13550-13583, 2023.

• Zi-Hao Qiu*, Quanqi Hu*, Yongjian Zhong, Lijun Zhang, and Tianbao Yang. Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. In Proceedings of the 39th International Conference on Machine Learning (ICML '22), pages 18122-18152, 2022. [paper] [code]

• Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, and Tianbao Yang. Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. In Proceedings of the 39th International Conference on Machine Learning (ICML’22), pages 25760-25782, 2022.

• Zi-Hao Qiu, Ying-Chun Jian, Qing-Guo Chen, and Lijun Zhang. Learning to Augment Imbalanced Data for Re-ranking Models. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM '21), pages 1478-1487, 2021.

Awards & Honors

• Excellent Graduate of Nanjing University. Nanjing, 2019

Projects

• I am a contributor of LibAUC , which is an open-source deep learning library for optimizing a wide spectrum of measures/losses. They can be organized into four categories: areas under the curves (e.g., AUROC, AUPRC), ranking meansures (e.g., mAP, NDCG), performance at the top (e.g., top-K variants of mAP and NDCG), and contrastive objectives.

Teaching Assistant

• Digital Image Processing. (For undergraduate staudent, Spring, 2020)

• Optimization Methods. (For graduate staudent, Fall, 2021)

• Computational Methods. (For undergraduate staudent, Spring, 2022)