1 citations · 1 across the 5 of their papers we have counts for
5 papers
Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation
Tao Wen, Elynn Chen, Yuzhou Chen +1
Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data. Despite state-of-the-art performance on graph classification tasks, GNNs are overwh…
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness
Qi Zhang, Yifei Wang, Jingyi Cui +4
Deep learning models often suffer from a lack of interpretability due to polysemanticity, where individual neurons are activated by multiple unrelated semantics, resulting in uncle…
Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning
Yijun Dong, Hoang Phan, Xiang Pan +1
We revisit data selection in a modern context of finetuning from a fundamental perspective. Extending the classical wisdom of variance minimization in low dimensions to high-dimens…
Bridging Domains with Approximately Shared Features
Ziliang Samuel Zhong, Xiang Pan, Qi Lei
Multi-source domain adaptation aims to reduce performance degradation when applying machine learning models to unseen domains. A fundamental challenge is devising the optimal strat…
Controllable Prompt Tuning For Balancing Group Distributional Robustness
Hoang Phan, Andrew Gordon Wilson, Qi Lei
Models trained on data composed of different groups or domains can suffer from severe performance degradation under distribution shifts. While recent methods have largely focused o…