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, 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), to appear, 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), to appear, 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)