8 citations · 22 across the 8 of their papers we have counts for
9 papers
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees
Guang-Yuan Hao, Hengguan Huang, Haotian Wang +2
Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domai…
Taxonomy-Structured Domain Adaptation
Tianyi Liu, Zihao Xu, Hao He +3
Domain adaptation aims to mitigate distribution shifts among different domains. However, traditional formulations are mostly limited to categorical domains, greatly simplifying nua…
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
Zihao Xu, Guang-Yuan Hao, Hao He +1
Previous studies have shown that leveraging domain index can significantly boost domain adaptation performance (arXiv:2007.01807, arXiv:2202.03628). However, such domain indices ar…
Domain Adaptation with Factorizable Joint Shift
Hao He, Yuzhe Yang, Hao Wang
Existing domain adaptation (DA) usually assumes the domain shift comes from either the covariates or the labels. However, in real-world applications, samples selected from differen…
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
Yuzhe Yang, Hao Wang, Dina Katabi
Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution.…
Continuously Indexed Domain Adaptation
Hao Wang, Hao He, Dina Katabi
Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously ind…