29 citations · 67 across the 24 of their papers we have counts for
6 papers
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Daoyuan Chen, Liuyi Yao, Dawei Gao +2
Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribut…
Counterfactual Debiasing for Generating Factually Consistent Text Summaries
Chenhe Dong, Yuexiang Xie, Yaliang Li +1
Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important y…
HPN: Personalized Federated Hyperparameter Optimization
Anda Cheng, Zhen Wang, Yaliang Li +1
Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and cha…
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
Qian Tao, Zhen Wang, Wenyuan Yu +2
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node…
Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
Yang Deng, Yaliang Li, Wenxuan Zhang +2
Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the cust…
Multi-source Hierarchical Prediction Consolidation
Chenwei Zhang, Sihong Xie, Yaliang Li +3
In big data applications such as healthcare data mining, due to privacy concerns, it is necessary to collect predictions from multiple information sources for the same instance, wi…