6 papers
Asymptotic Probabilities of Attaining the Maximum in Heterogeneous Gaussian Samples
Chunxu Zhang, Baiqi Miao, Tiantian Mao
We study asymptotic probabilities of attaining the maximum in heterogeneous Gaussian samples. In the two-group setting, the first sample has variance and size , while the…
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
Zhiwei Li, Guodong Long, Chunxu Zhang +3
Integrating Foundation Models (FMs) into recommendation systems is an emerging and promising research direction. However, centralized paradigms face growing pressure from privacy c…
Learning Evolving Preferences: A Federated Continual Framework for User-Centric Recommendation
Chunxu Zhang, Zhiheng Xue, Guodong Long +2
User-centric recommendation has become essential for delivering personalized services, as it enables systems to adapt to users' evolving behaviors while respecting their long-term…
Wasserstein Distributionally Robust Quantile Regression
Chunxu Zhang, Tiantian Mao, Ruodu Wang
We study distributionally robust quantile regression using type- Wasserstein ambiguity sets. We derive a closed-form expression for the worst-case quantile regression loss under…
Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
Jundong Chen, Honglei Zhang, Chunxu Zhang +2
Federated recommendation (FR) facilitates collaborative training by aggregating local models from massive devices, enabling client-specific personalization while ensuring privacy.…
Beyond Personalization: Federated Recommendation with Calibration via Low-rank Decomposition
Jundong Chen, Honglei Zhang, Haoxuan Li +3
Federated recommendation (FR) is a promising paradigm to protect user privacy in recommender systems. Distinct from general federated scenarios, FR inherently needs to preserve cli…