3 papers
cs.LG2025
Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition
Tian-Shuang Wu, Shen-Huan Lyu, Ning Chen +2
In recent years, as a compromise between privacy and performance, few-sample model compression has been widely adopted to deal with limited data resulting from privacy and security…
cs.LG2025
Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation
Shen-Huan Lyu, Yi-Xiao He, Yanyan Wang +3
Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been prov…
cs.LG2025
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
Ning Chen, Shen-Huan Lyu, Tian-Shuang Wu +2
In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing la…