3 papers
stat.ML2026
Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting
Yuhang Jiang, Fengchuan Zhang, Sanguo Zhang +1
Domain generalization (DG) aims to learn from multiple source domains and generalize to unseen target domains. Most DG methods pursue invariance: they seek a causal representation…
stat.ML2026
Deep Multitask Learning for Mixed-Type Outcomes with Shared Sparsity
Huichao Li, Tong Wang, Sanguo Zhang +1
Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome. When outcomes differ acros…
stat.ME2026
Model-agnostic information transfer and fusion for classification with label noise
Zhu Guojun, Zhang Sanguo, Ren Mingyang
Label noise presents a fundamental challenge in modern machine learning, especially when large-scale datasets are generated via automated processes. An increasingly common and impo…