15 citations · 28 across the 11 of their papers we have counts for
6 papers · 1 filter
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models
Kaican Li, Weiyan Xie, Yongxiang Huang +5
Fine-tuning foundation models often compromises their robustness to distribution shifts. To remedy this, most robust fine-tuning methods aim to preserve the pre-trained features. H…
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…
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Runpeng Yu, Hong Zhu, Kaican Li +5
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms…
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
Nanyang Ye, Kaican Li, Haoyue Bai +5
Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically…