9 citations · 9 across the 8 of their papers we have counts for
9 papers
How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark
Yingxuan Cheng, Tong Li, Yayong Li +5
Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) require validation before use i…
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…
Favourable Missingness in Semi-Supervised Classification for Exponential Mixture Models
Huanchao Zhou, Jinran Wu, Fariborz Setoudehtazang +1
Semi-supervised classifiers are commonly trained from samples in which all features are observed but some class labels are missing. When label missingness is independent of the obs…
Robust Deep Mixture Models
Jinran Wu, Geoffrey J. McLachlan
We propose a robust deep mixture model based on a pathway-wise shared scale-mixture construction. Layer-specific component indicators are independently distributed according to the…
Module-structured mixture factor models for molecular subtype discovery in transcriptomic data
Jinran Wu, Geoffrey J. McLachlan, Saumyadipta Pyne
High-throughput gene expression data exhibit high dimensionality, complex intergene dependence, and pronounced biological heterogeneity across samples, presenting major challenges…
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…
SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness
Geoffrey J. McLachlan, Jinran Wu
Partially labelled samples arise when features are observed for data, but class labels are available for only a subset. In such settings, the mechanism governing label availability…