arXiv Statistics ML August 21, 2026 By Geoffrey J. McLachlan, Jinran Wu

SSLfmm: An R Package for Semi-Supervised Learning with Mixed Missingness

Read the original on arXiv Statistics ML →

arXiv:2512. 03322v4 Announce Type: replace-cross Abstract: Partially labelled samples arise when features are observed for all data, but class labels are available for only a subset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

arXiv Statistics ML

Aug 26

Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification

arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of...

By You-Gan Wang, Jinran Wu, Geoffrey J. McLachlan

benchmarks

arXiv Machine Learning

Sep 2

Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models

arXiv:2609.00774v1 Announce Type: cross Abstract: We consider semi-supervised classification from a partially classified sample arising from a two-component Weibull mixture. The feature is observed f...

By Jinran Wu, You-Gan Wang, Geoffrey J. McLachlan

arXiv Machine Learning

Sep 1

Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk

arXiv:2608.30561v1 Announce Type: cross Abstract: Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of m...

By Fariborz Setoudehtazang, Geoffrey J. McLachlan

arXiv Machine Learning

Jul 24

One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

arXiv:2607. 20641v1 Announce Type: new Abstract: Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data.

By Afsaneh Mahanipour, Hana Khamfroush

computer-vision benchmarks safety

arXiv Machine Learning

Jun 25

Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning

arXiv:2606. 25770v1 Announce Type: new Abstract: Data scarcity is a major bottleneck in medical Multiple Instance Learning (MIL), especially for rare diseases or expensive modalities.

By Muhammed Furkan Dasdelen, Fatih Ozlugedik, Anastasia Litinetskaya, Nassir Navab, Carsten Marr, Ario Sadafi

rag safety

arXiv Machine Learning

Jun 5

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data

arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.

By Ziwen Kan, Wugeng Zheng, Tianlong Chen, Song Wang

rag diffusion multimodal benchmarks

One email a morning, machine-written

Related stories

Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification

Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models

Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk

One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification

Re-mixing Embeddings for Patient Augmentation in Data Scarce Multiple Instance Learning

PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data