Learning to Augment Imbalanced Data for Re-ranking Models. - Zi-Hao Qiu; Ying-Chun Jian; Qing-Guo Chen; Lijun Zhang 005 (CIKM, 2021)

Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. - Zi-Hao Qiu; Quanqi Hu; Yongjian Zhong; Lijun Zhang 005; Tianbao Yang (ICML, 2022)

Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. - Zhuoning Yuan; Yuexin Wu; Zi-Hao Qiu; Xianzhi Du; Lijun Zhang 005; Denny Zhou; Tianbao Yang (ICML, 2022)

Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. - Quanqi Hu; Zi-Hao Qiu; Zhishuai Guo; Lijun Zhang 005; Tianbao Yang (ICML, 2023)

Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. - Zi-Hao Qiu; Quanqi Hu; Zhuoning Yuan; Denny Zhou; Lijun Zhang 005; Tianbao Yang (ICML, 2023)

LibAUC: A Deep Learning Library for X-Risk Optimization. - Zhuoning Yuan; Dixian Zhu; Zi-Hao Qiu; Gang Li; Xuanhui Wang; Tianbao Yang (KDD, 2023)

To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. - Zi-Hao Qiu; Siqi Guo 003; Mao Xu; Tuo Zhao; Lijun Zhang 005; Tianbao Yang (ICML, 2024)

Optimal large-scale stochastic optimization of NDCG surrogates for deep learning. - Zi-Hao Qiu; Quanqi Hu; Yongjian Zhong; Wei-Wei Tu; Lijun Zhang 005; Tianbao Yang (Mach. Learn., 2025)