45 citations · 146 across the 11 of their papers we have counts for
11 papers
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang, Yuchen Yan, Xi Zhang +4
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-…
A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks
Ved Piyush, Yuchen Yan, Yuzhen Zhou +2
Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing. Although conventional DLs get tra…
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
Dingsu Wang, Yuchen Yan, Ruizhong Qiu +4
Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generat…
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
Ruijie Wang, Baoyu Li, Yichen Lu +6
This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \t…
Continual Learning on Dynamic Graphs via Parameter Isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li +4
Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetti…
Neural Exploitation and Exploration of Contextual Bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee +1
In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decad…
STERLING: Synergistic Representation Learning on Bipartite Graphs
Baoyu Jing, Yuchen Yan, Kaize Ding +4
A fundamental challenge of bipartite graph representation learning is how to extract informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to address…
COIN: Co-Cluster Infomax for Bipartite Graphs
Baoyu Jing, Yuchen Yan, Yada Zhu +1
Bipartite graphs are powerful data structures to model interactions between two types of nodes, which have been used in a variety of applications, such as recommender systems, info…
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee +1
In this paper, we propose a novel neural exploration strategy in contextual bandits, EE-Net, distinct from the standard UCB-based and TS-based approaches. Contextual multi-armed ba…
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Jinning Li, Huajie Shao, Dachun Sun +6
This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space a…
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Ruijie Wang, Yuchen Yan, Jialu Wang +4
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale…