120 citations · 127 across the 4 of their papers we have counts for
4 papers
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Fan Bao, Guoqiang Wu, Chongxuan Li +2
The (gradient-based) bilevel programming framework is widely used in hyperparameter optimization and has achieved excellent performance empirically. Previous theoretical work mainl…
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
Guoqiang Wu, Chongxuan Li, Kun Xu +1
(Partial) ranking loss is a commonly used evaluation measure for multi-label classification, which is usually optimized with convex surrogates for computational efficiency. Prior t…
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
Guoqiang Wu, Jun Zhu
Various evaluation measures have been developed for multi-label classification, including Hamming Loss (HL), Subset Accuracy (SA) and Ranking Loss (RL). However, there is a gap bet…
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
Guoqiang Wu, Ruobing Zheng, Yingjie Tian +1
Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims…