10.1k citations · 12.1k across the 55 of their papers we have counts for
57 papers
Federated Semantic Residuals for Multimodal Recommendation
Zhiwei Li, Guodong Long, Chengqi Zhang +1
Federated Recommendation Systems (FedRSs) learn collaborative scores from interaction histories on device and rank items by them, so a higher score means a higher rank. Item text a…
Symmetric Bases and Embeddings in Musielak--Orlicz Sequence Spaces under
Valentin Georgiev, Boyan Zlatanov
We study isomorphic embeddings into Musielak-Orlicz sequence spaces \(\ell_Φ\), where the normalized generating sequence \(Φ=\{ϕ_n\}\) satisfies the \(δ_2\) condition. Using admiss…
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…
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Zicheng Zhao, Linhao Luo, Shirui Pan +2
Knowledge graphs (KGs) store enormous facts as relationships between entities. Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing inte…
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Zhiwei Li, Guodong Long, Tianyi Zhou +2
Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy in a federated setting. Existing FedCF m…
Navigating the Future of Federated Recommendation Systems with Foundation Models
Zhiwei Li, Guodong Long, Chunxu Zhang +3
Federated Recommendation Systems (FRSs) offer a privacy-preserving alternative to traditional centralized approaches by decentralizing data storage. However, they face persistent c…
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
Shengchao Chen, Guodong Long, Jing Jiang +1
This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-Weather, a generic appro…
Personalized Additive Modeling for Multi-level Federated Learning
Shutong Chen, Guodong Long, Tianyi Zhou +3
Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous clie…
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
Zhihong Deng, Jing Jiang, Guodong Long +1
In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maxi…
Seeing Text in the Dark: Algorithm and Benchmark
Chengpei Xu, Hao Fu, Long Ma +6
Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancem…
Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey
Shengchao Chen, Guodong Long, Jing Jiang +2
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive develop…
Does Continual Learning Equally Forget All Parameters?
Haiyan Zhao, Tianyi Zhou, Guodong Long +2
Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedl…
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
Haiyan Zhao, Tianyi Zhou, Guodong Long +2
As a few large-scale pre-trained models become the major choices of various applications, new challenges arise for model pruning, e.g., can we avoid pruning the same model from scr…
Dual Personalization on Federated Recommendation
Chunxu Zhang, Guodong Long, Tianyi Zhou +4
Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings. Existing solutions are used t…
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Yue Tan, Yixin Liu, Guodong Long +3
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing to the advantages of federated learning, federated graph learning (FGL) enables clients to t…
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection
Shaoshen Wang, Yanbin Liu, Ling Chen +1
Unsupervised anomaly detection (AD) is a challenging task in realistic applications. Recently, there is an increasing trend to detect anomalies with deep neural networks (DNN). How…
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Yunqiu Xu, Meng Fang, Ling Chen +3
Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, th…
On the Convergence of Clustered Federated Learning
Jie Ma, Guodong Long, Tianyi Zhou +2
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1)…
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Yunqiu Xu, Meng Fang, Ling Chen +2
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the general…
Federated Learning for Open Banking
Guodong Long, Yue Tan, Jing Jiang +1
Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial servi…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent cl…
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Zonghan Wu, Shirui Pan, Guodong Long +2
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings.…
FedProto: Federated Prototype Learning across Heterogeneous Clients
Yue Tan, Guodong Long, Lu Liu +4
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occur…
Isometric Propagation Network for Generalized Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +3
Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is…