5 citations · 13 across the 13 of their papers we have counts for
13 papers
Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection
Wenxuan Bao, Yanjun Zhao, Xiyuan Yang +1
Pretrained vision-language models such as CLIP exhibit strong zero-shot generalization but remain sensitive to distribution shifts. Test-time adaptation adapts models during infere…
FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation
Xinrui He, Ting-Wei Li, Tianxin Wei +5
Federated cross-domain recommendation (Federated CDR) aims to collaboratively learn personalized recommendation models across heterogeneous domains while preserving data privacy. R…
Panda: Test-Time Adaptation with Negative Data Augmentation
Ruxi Deng, Wenxuan Bao, Tianxin Wei +1
Pretrained VLMs exhibit strong zero-shot classification capabilities, but their predictions degrade significantly under common image corruptions. To improve robustness, many test-t…
Mint: A Simple Test-Time Adaptation of Vision-Language Models against Common Corruptions
Wenxuan Bao, Ruxi Deng, Jingrui He
Pretrained vision-language models such as CLIP achieve strong zero-shot generalization but remain vulnerable to distribution shifts caused by input corruptions. In this work, we in…
Connecting Domains and Contrasting Samples: A Ladder for Domain Generalization
Tianxin Wei, Yifan Chen, Xinrui He +2
Distribution shifts between training and testing samples frequently occur in practice and impede model generalization performance. This crucial challenge thereby motivates studies…
Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation
Wenxuan Bao, Zhichen Zeng, Zhining Liu +2
Powerful as they are, graph neural networks (GNNs) are known to be vulnerable to distribution shifts. Recently, test-time adaptation (TTA) has attracted attention due to its abilit…
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Rishub Tamirisa, Chulin Xie, Wenxuan Bao +3
Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via persona…
Adaptive Test-Time Personalization for Federated Learning
Wenxuan Bao, Tianxin Wei, Haohan Wang +1
Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts. However, most of these methods require labeled data on te…
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
Dongqi Fu, Wenxuan Bao, Ross Maciejewski +2
In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end…
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
Wenxuan Bao, Haohan Wang, Jun Wu +1
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL…
Understanding the Effect of Data Augmentation on Knowledge Distillation
Ziqi Wang, Chi Han, Wenxuan Bao +1
Knowledge distillation (KD) requires sufficient data to transfer knowledge from large-scale teacher models to small-scale student models. Therefore, data augmentation has been wide…
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Wenxuan Bao, Jun Wu, Jingrui He
In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically dis…
Deep Grouping Model for Unified Perceptual Parsing
Zhiheng Li, Wenxuan Bao, Jiayang Zheng +1
The perceptual-based grouping process produces a hierarchical and compositional image representation that helps both human and machine vision systems recognize heterogeneous visual…