3 papers
cs.LG2026
Environment-Conditioned Tail Reweighting for Total Variation Invariant Risk Minimization
Yuanchao Wang, Zhao-Rong Lai, Tianqi Zhong +1
Out-of-distribution (OOD) generalization remains challenging when models simultaneously encounter correlation shifts across environments and diversity shifts driven by rare or hard…
cs.CV2025
BootOOD: Self-Supervised Out-of-Distribution Detection via Synthetic Sample Exposure under Neural Collapse
Yuanchao Wang, Tian Qin, Eduardo Valle +1
Out-of-distribution (OOD) detection is critical for deploying image classifiers in safety-sensitive environments, yet existing detectors often struggle when OOD samples are semanti…
cs.LG2025
Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization
Yuanchao Wang, Zhao-Rong Lai, Tianqi Zhong
Invariant risk minimization is an important general machine learning framework that has recently been interpreted as a total variation model (IRM-TV). However, how to improve out-o…