3 papers
cs.LG2026
Supervised Graph Contrastive Learning for Gene Regulatory Networks
Sho Oshima, Yuji Okamoto, Taisei Tosaki +1
Graph Contrastive Learning (GCL) is a powerful self-supervised learning framework that performs data augmentation through graph perturbations, with growing applications in the anal…
cs.LG2026
A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency
Nanae Aratake, Taisei Tosaki, Yuji Okamoto +5
Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-s…
cs.LG2025
Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima +9
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for use is often hindered by datase…