Coreset Stochastic Variance-Reduced Gradient with Application to Optimal Margin Distribution Machine. - Zhi-Hao Tan; Teng Zhang 001; Wei Wang 028 (AAAI, 2019)

Multi-label optimal margin distribution machine. - Zhi-Hao Tan; Peng Tan 002; Yuan Jiang 001; Zhi-Hua Zhou (Mach. Learn., 2020)

Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks. - Zhi-Hao Tan; Yi Xie; Yuan Jiang 001; Zhi-Hua Zhou (NeurIPS, 2022)

Identifying Helpful Learnwares Without Examining the Whole Market. - Yi Xie; Zhi-Hao Tan; Yuan Jiang 001; Zhi-Hua Zhou (ECAI, 2023)

Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data. - Peng Tan 002; Zhi-Hao Tan; Yuan Jiang 001; Zhi-Hua Zhou (IJCAI, 2023)

Towards Making Learnware Specification and Market Evolvable. - Jian-Dong Liu; Zhi-Hao Tan; Zhi-Hua Zhou (AAAI, 2024)

Beimingwu: A Learnware Dock System. - Zhi-Hao Tan; Jian-Dong Liu; Xiao-Dong Bi; Peng Tan 002; Qin-Cheng Zheng; Hai-Tian Liu; Yi Xie; Xiao-Chuan Zou; Yang Yu 001; Zhi-Hua Zhou (KDD, 2024)

On the Ability of Developers' Training Data Preservation of Learnware. - Hao-Yi Lei; Zhi-Hao Tan; Zhi-Hua Zhou (NeurIPS, 2024)

Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. - Peng Tan 002; Hai-Tian Liu; Zhi-Hao Tan; Zhi-Hua Zhou (NeurIPS, 2024)

Learnware: small models do big. - Zhi-Hua Zhou; Zhi-Hao Tan (Sci. China Inf. Sci., 2024)

Towards enabling learnware to handle heterogeneous feature spaces. - Peng Tan 002; Zhi-Hao Tan; Yuan Jiang 001; Zhi-Hua Zhou (Mach. Learn., 2024)

Learnware of Language Models: Specialized Small Language Models Can Do Big. - Zhi-Hao Tan; Zi-Chen Zhao; Hao-Yu Shi; Xin-Yu Zhang; Peng Tan 002; Yang Yu 001; Zhi-Hua Zhou (CoRR, 2025)