236 citations · 263 across the 11 of their papers we have counts for
11 papers
A Survey on Evaluation of Out-of-Distribution Generalization
Han Yu, Jiashuo Liu, Xingxuan Zhang +2
Machine learning models, while progressively advanced, rely heavily on the IID assumption, which is often unfulfilled in practice due to inevitable distribution shifts. This render…
Flatness-Aware Minimization for Domain Generalization
Xingxuan Zhang, Renzhe Xu, Han Yu +3
Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explore…
Meta Adaptive Task Sampling for Few-Domain Generalization
Zheyan Shen, Han Yu, Peng Cui +4
To ensure the out-of-distribution (OOD) generalization performance, traditional domain generalization (DG) methods resort to training on data from multiple sources with different u…
Learning to Code on Graphs for Topological Interference Management
Zhiwei Shan, Xinping Yi, Han Yu +2
The state-of-the-art coding schemes for topological interference management (TIM) problems are usually handcrafted for specific families of network topologies, relying critically o…
Rethinking the Evaluation Protocol of Domain Generalization
Han Yu, Xingxuan Zhang, Renzhe Xu +3
Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization by leveraging common knowledge learned from multiple training domains to generalize to…
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Xingxuan Zhang, Renzhe Xu, Han Yu +2
Recently, flat minima are proven to be effective for improving generalization and sharpness-aware minimization (SAM) achieves state-of-the-art performance. Yet the current definiti…
On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey
Xu Guo, Han Yu
Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). These PLMs have brought significant performance gains for a range of NLP tasks, circ…
FIXED: Frustratingly Easy Domain Generalization with Mixup
Wang Lu, Jindong Wang, Han Yu +4
Domain generalization (DG) aims to learn a generalizable model from multiple training domains such that it can perform well on unseen target domains. A popular strategy is to augme…
Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation
Xu Guo, Boyang Li, Han Yu
Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP t…
Measure-Theoretic Probability of Complex Co-occurrence and E-Integral
Jian-Yong Wang, Han Yu
Complex high-dimensional co-occurrence data are increasingly popular from a complex system of interacting physical, biological and social processes in discretely indexed modifiable…
Towards Out-Of-Distribution Generalization: A Survey
Jiashuo Liu, Zheyan Shen, Yue He +4
Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Inde…