8 citations · 18 across the 4 of their papers we have counts for
4 papers
Towards Understanding Generalization of Macro-AUC in Multi-label Learning
Guoqiang Wu, Chongxuan Li, Yilong Yin
Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solv…
Toward Understanding Generative Data Augmentation
Chenyu Zheng, Guoqiang Wu, Chongxuan Li
Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various lea…
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Chenyu Zheng, Guoqiang Wu, Fan Bao +3
A large-scale deep model pre-trained on massive labeled or unlabeled data transfers well to downstream tasks. Linear evaluation freezes parameters in the pre-trained model and trai…
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Shuyu Cheng, Guoqiang Wu, Jun Zhu
Zeroth-order (ZO) optimization is widely used to handle challenging tasks, such as query-based black-box adversarial attacks and reinforcement learning. Various attempts have been…