345 citations · 840 across the 61 of their papers we have counts for
20 papers · 1 filter
Causal Fine-Tuning under Latent Confounded Shift
Jialin Yu, Yuxiang Zhou, Haoxuan Li +6
Adapting to latent confounded shift remains a core challenge in modern AI. This setting is driven by hidden variables that induce spurious correlations between inputs and outputs d…
Resilient Practical Test-Time Adaptation: Soft Batch Normalization Alignment and Entropy-driven Memory Bank
Xingzhi Zhou, Zhiliang Tian, Ka Chun Cheung +2
Test-time domain adaptation effectively adjusts the source domain model to accommodate unseen domain shifts in a target domain during inference. However, the model performance can…
Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts
Kaican Li, Yifan Zhang, Lanqing Hong +2
Out-of-distribution (OOD) generalization is a complicated problem due to the idiosyncrasies of possible distribution shifts between training and test domains. Most benchmarks emplo…
A Causal Framework to Unify Common Domain Generalization Approaches
Nevin L. Zhang, Kaican Li, Han Gao +5
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem…
Consistency Regularization for Domain Generalization with Logit Attribution Matching
Han Gao, Kaican Li, Weiyan Xie +5
Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source sett…
Adaptive Label Smoothing with Self-Knowledge in Natural Language Generation
Dongkyu Lee, Ka Chun Cheung, Nevin L. Zhang
Overconfidence has been shown to impair generalization and calibration of a neural network. Previous studies remedy this issue by adding a regularization term to a loss function, p…