179 citations · 535 across the 16 of their papers we have counts for
16 papers
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Peiyan Zhang, Haoyang Liu, Chaozhuo Li +3
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in…
ConvFormer: Revisiting Transformer for Sequential User Modeling
Hao Wang, Jianxun Lian, Mingqi Wu +5
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behav…
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li, Xu Chen, Chaozhuo Li +8
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize int…
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…
Test-Time Training for Graph Neural Networks
Yiqi Wang, Chaozhuo Li, Wei Jin +4
Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task. However, a performance gap between the training set and the test set has often been not…
Learning on Large-scale Text-attributed Graphs via Variational Inference
Jianan Zhao, Meng Qu, Chaozhuo Li +5
This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both…
Geometric Interaction Augmented Graph Collaborative Filtering
Yiding Zhang, Chaozhuo Li, Senzhang Wang +2
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly resear…
Domain-invariant Feature Exploration for Domain Generalization
Wang Lu, Jindong Wang, Haoliang Li +2
Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (…
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
Jinyi Hu, Xiaoyuan Yi, Wenhao Li +2
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks. However, due to the sequential nature of the text, auto-regressive de…
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Peiyan Zhang, Jiayan Guo, Chaozhuo Li +6
Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various…
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Jiayan Guo, Peiyan Zhang, Chaozhuo Li +3
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions. Recently, there has been an increasing interest in modeling the user preferen…
HousE: Knowledge Graph Embedding with Householder Parameterization
Rui Li, Jianan Zhao, Chaozhuo Li +9
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can on…
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Yiqi Wang, Chaozhuo Li, Zheng Liu +5
Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffe…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…
Localized Graph Collaborative Filtering
Yiqi Wang, Chaozhuo Li, Mingzheng Li +5
User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learni…
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang +5
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified i…