16 citations · 35 across the 14 of their papers we have counts for
14 papers
Graph Domain Adaptation via Homophily-Agnostic Reconstructing Structure
Ruiyi Fang, Shuo Wang, Ruizhi Pu +8
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. However, existing GDA methods t…
Homophily Enhanced Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Jingyu Zhao +5
Graph Domain Adaptation (GDA) transfers knowledge from labeled source graphs to unlabeled target graphs, addressing the challenge of label scarcity. In this paper, we highlight the…
On the Benefits of Attribute-Driven Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Zhao Kang +5
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Rece…
ZETA: Leveraging Z-order Curves for Efficient Top-k Attention
Qiuhao Zeng, Jerry Huang, Peng Lu +4
Over recent years, the Transformer has become a fundamental building block for sequence modeling architectures. Yet at its core is the use of self-attention, whose memory and compu…
Intersectional Unfairness Discovery
Gezheng Xu, Qi Chen, Charles Ling +2
AI systems have been shown to produce unfair results for certain subgroups of population, highlighting the need to understand bias on certain sensitive attributes. Current research…
Generalizing across Temporal Domains with Koopman Operators
Qiuhao Zeng, Wei Wang, Fan Zhou +7
In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. Thi…
Hessian Aware Low-Rank Perturbation for Order-Robust Continual Learning
Jiaqi Li, Yuanhao Lai, Rui Wang +7
Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this work, we propose the Hessian Aware Low-Ran…
Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization
Ganyu Wang, Qingsong Zhang, Li Xiang +3
Vertical Federated Learning (VFL) attracts increasing attention because it empowers multiple parties to jointly train a privacy-preserving model over vertically partitioned data. R…
Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation
Pengcheng Xu, Boyu Wang, Charles Ling
Current methods of blended targets domain adaptation (BTDA) usually infer or consider domain label information but underemphasize hybrid categorical feature structures of targets,…
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
Qiuhao Zeng, Wei Wang, Fan Zhou +2
Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution…
Source-Free Domain Adaptation for Question Answering with Masked Self-training
M. Yin, B. Wang, Y. Dong +1
Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source…
On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui, Gezheng Xu, Qi Chen +5
We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenar…
Evolving Domain Generalization
William Wei Wang, Gezheng Xu, Ruizhi Pu +6
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any…
Aggregating From Multiple Target-Shifted Sources
Changjian Shui, Zijian Li, Jiaqi Li +3
Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. Hence, a crucial aspect is to properly combine different…