Di Wu

TL;DR: An empirical study on heterogeneous defect prediction, finding that metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability and that the CTKCCA approach currently has the best performance.

TL;DR: Experimental results show that LTDD can better improve the fairness of ML software with less or comparable damage to the performance, and LTDD is more actionable for fairness improvement in realistic scenarios.

TL;DR: Wang et al. as discussed by the authors proposed sample discrimination based selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance.

TL;DR: This paper proposes a novel multi-kernel DL approach, named MKLDPL, which jointly learns a kernel synthesis dictionary and a kernel analysis dictionary by exploiting the class label information and imposes the low-rank regularization on the analysis dictionary.