9 citations · 9 across the 9 of their papers we have counts for
10 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…
Large Classification-Risk-Optional Label Acquisition
F. Setoudehtanzangi, Geoffrey J. McLachlan
We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification problems in which features are…
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…
Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk
Fariborz Setoudehtazang, Geoffrey J. McLachlan
Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of missing labels may itself carry…
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…
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…