9 citations · 9 across the 5 of their papers we have counts for
7 papers
Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
We consider semi-supervised classification from a partially classified sample arising from a two-component Weibull mixture. The feature is observed for all data, whereas some class…
Deep Skew-t Mixture Models
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew- mixture model (DStMM), a hier…
Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
You-Gan Wang, Jinran Wu, Geoffrey J. McLachlan
Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of label missingness depends on t…
Informative missingness and its implications in semi-supervised learning
Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan
Semi-supervised learning (SSL) constructs classifiers using both labelled and unlabelled data. It leverages information from labelled samples, whose acquisition is often costly or…
A Differential Index Measuring Rater's Capability in Educational Assessment
Y. -G. Wang, J. Wu, X. Qiu
A rater's ability to assign accurate scores can significantly impact the outcomes of educational assessments. However, common indices for evaluating rater characteristics typically…
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
Yilong Hou, Xi'an Li, Jinran Wu +1
Bayesian Physics Informed Neural Networks (BPINN) have attracted considerable attention for inferring the system states and physical parameters of differential equations according…