21 citations · 37 across the 6 of their papers we have counts for
6 papers
Denoising Time Cycle Modeling for Recommendation
Sicong Xie, Qunwei Li, Weidi Xu +3
Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal p…
Edge-cloud Collaborative Learning with Federated and Centralized Features
Zexi Li, Qunwei Li, Yi Zhou +3
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud serve…
Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations
Ningning Li, Qunwei Li, Xichen Ding +2
Deep Candidate Generation plays an important role in large-scale recommender systems. It takes user history behaviors as inputs and learns user and item latent embeddings for candi…
Quickest Change Detection in Anonymous Heterogeneous Sensor Networks
Zhongchang Sun, Shaofeng Zou, Ruizhi Zhang +1
The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied. There are heterogeneous sensors and a fusion center. The sensors are clust…
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization
Ziyi Chen, Zhengyang Hu, Qunwei Li +2
Gradient descent-ascent (GDA) is a widely used algorithm for minimax optimization. However, GDA has been proved to converge to stationary points for nonconvex minimax optimization,…
Learning Graph Neural Networks with Approximate Gradient Descent
Qunwei Li, Shaofeng Zou, Wenliang Zhong
The first provably efficient algorithm for learning graph neural networks (GNNs) with one hidden layer for node information convolution is provided in this paper. Two types of GNNs…