| 2024 | ICML | To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. | Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang |
| 2023 | ICML | Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization. | Quanqi Hu, Zi-Hao Qiu, Zhishuai Guo, Lijun Zhang, Tianbao Yang |
| 2023 | ICML | 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, Tianbao Yang |
| 2023 | KDD | LibAUC: A Deep Learning Library for X-Risk Optimization. | Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang |
| 2022 | ICML | Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. | Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, Tianbao Yang |
| 2022 | ICML | Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. | Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, Tianbao Yang |
| 2021 | CIKM | Learning to Augment Imbalanced Data for Re-ranking Models. | Zi-Hao Qiu, Ying-Chun Jian, Qing-Guo Chen, Lijun Zhang |