7 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Test-Time Adaptation by Causal Trimming
Yingnan Liu, Rui Qiao, Mong Li Lee +1
Test-time adaptation aims to improve model robustness under distribution shifts by adapting models with access to unlabeled target samples. A primary cause of performance degradati…
cs.CV2024★ 7 cited
Cross-Domain Feature Augmentation for Domain Generalization
Yingnan Liu, Yingtian Zou, Rui Qiao +3
Domain generalization aims to develop models that are robust to distribution shifts. Existing methods focus on learning invariance across domains to enhance model robustness, and d…
cs.LG2024
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
Yingtian Zou, Kenji Kawaguchi, Yingnan Liu +3
Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different…