25 citations · 56 across the 23 of their papers we have counts for
24 papers
: Unifying Scale-Specific Structures and Prototype Decomposition for Spatial-Temporal Forecasting
Chengyang Zhou, Zijian Zhang, Hao Miao +5
Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of spatial dynamics and temporal non-stationarity. Ex…
A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Chunxu Zhang, Bo Li, Wenliang Wang +7
Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order…
SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation
Chunxu Zhang, Shanqiang Huang, Zijian Zhang +5
Cross-domain recommendation (CDR) addresses the data sparsity and cold-start problems in the target domain by leveraging knowledge from data-rich source domains. However, existing…
FedDis: A Causal Disentanglement Framework for Federated Traffic Prediction
Chengyang Zhou, Zijian Zhang, Chunxu Zhang +4
Federated learning offers a promising paradigm for privacy-preserving traffic prediction, yet its performance is often challenged by the non-identically and independently distribut…
TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
Mingyuan Xia, Chunxu Zhang, Zijian Zhang +4
Temporal non-stationarity, the phenomenon that time series distributions change over time, poses fundamental challenges to reliable time series forecasting. Intuitively, the comple…
Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
Chunxu Zhang, Weipeng Zhang, Guodong Long +3
Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner. How to integrate multi-modality features into federat…
Distilling A Universal Expert from Clustered Federated Learning
Zeqi Leng, Chunxu Zhang, Guodong Long +2
Clustered Federated Learning (CFL) addresses the challenges posed by non-IID data by training multiple group- or cluster-specific expert models. However, existing methods often ove…
Personalized Recommendation Models in Federated Settings: A Survey
Chunxu Zhang, Guodong Long, Zijian Zhang +4
Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experie…
Incomplete Graph Learning: A Comprehensive Survey
Riting Xia, Huibo Liu, Anchen Li +4
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods ar…
LLM-Powered User Simulator for Recommender System
Zijian Zhang, Shuchang Liu, Ziru Liu +6
User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerati…
Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach
Chunxu Zhang, Guodong Long, Hongkuan Guo +5
Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, p…
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions
Jing Jiang, Chunxu Zhang, Honglei Zhang +3
Personalization stands as the cornerstone of recommender systems (RecSys), striving to sift out redundant information and offer tailor-made services for users. However, the convent…
Efficient and Robust Regularized Federated Recommendation
Langming Liu, Wanyu Wang, Xiangyu Zhao +9
Recommender systems play a pivotal role across practical scenarios, showcasing remarkable capabilities in user preference modeling. However, the centralized learning paradigm predo…
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…
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
Zijian Zhang, Shuchang Liu, Jiaao Yu +9
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehen…
Federated Adaptation for Foundation Model-based Recommendations
Chunxu Zhang, Guodong Long, Hongkuan Guo +7
With the recent success of large language models, particularly foundation models with generalization abilities, applying foundation models for recommendations becomes a new paradig…
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
Zijian Zhang, Xiangyu Zhao, Qidong Liu +6
In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic st…
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
Chunxu Zhang, Guodong Long, Tianyi Zhou +3
Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recom…
GPFedRec: Graph-guided Personalization for Federated Recommendation
Chunxu Zhang, Guodong Long, Tianyi Zhou +3
The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner. Using user-relation graphs to enhan…
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Zijian Zhang, Xiangyu Zhao, Hao Miao +3
Spatio-Temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integra…
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…
Curvature Regularization to Prevent Distortion in Graph Embedding
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang +2
Recent research on graph embedding has achieved success in various applications. Most graph embedding methods preserve the proximity in a graph into a manifold in an embedding spac…
Hall conductivity as topological invariant in phase space
I. V. Fialkovsky, M. Suleymanov, Xi Wu +2
It is well known that the quantum Hall conductivity in the presence of constant magnetic field is expressed through the topological TKNN invariant. The same invariant is responsibl…
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks
Chunxu Zhang, Jiaxu Cui, Bo Yang
In recent years, deep learning has achieved remarkable achievements in many fields, including computer vision, natural language processing, speech recognition and others. Adequate…