Qitian Wu

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 59

Top Topics

Graph Representations

Mutual Reinforcement

Top Venues

Publications

Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, Junchi Yan
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions. WWW (2024)
Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan
GeoMix: Towards Geometry-Aware Data Augmentation. CoRR (2024)
Qitian Wu, Fan Nie, Chenxiao Yang, Junchi Yan
Learning Divergence Fields for Shift-Robust Graph Representations. CoRR (2024)
Tianqi Zhang, Qitian Wu, Junchi Yan, Yunan Zhao, Bing Han
ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation. IEEE Trans. Neural Networks Learn. Syst. 35 (4) (2024)
Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan
Graph Out-of-Distribution Generalization via Causal Intervention. CoRR (2024)
Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan
GeoMix: Towards Geometry-Aware Data Augmentation. KDD (2024)
Yang Fan, Xiangping Wu, Qingcai Chen, Heng Li, Yan Huang, Zhixiang Cai, Qitian Wu
TDeLTA: A Light-Weight and Robust Table Detection Method Based on Learning Text Arrangement. AAAI (2024)
Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan
Graph Out-of-Distribution Generalization via Causal Intervention. WWW (2024)
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. ICLR (2023)
Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, Junchi Yan
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions. CoRR (2023)
Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks. CoRR (2023)
Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan
Simplifying and Empowering Transformers for Large-Graph Representations. CoRR (2023)
Yang Fan, Xiangping Wu, Qingcai Chen, Heng Li, Yan Huang, Zhixiang Cai, Qitian Wu
TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement. CoRR (2023)
Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift. NeurIPS (2023)
Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks. KDD (2023)
Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Fan Nie, Michael Bronstein, Junchi Yan
Advective Diffusion Transformers for Topological Generalization in Graph Learning. CoRR (2023)
Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan
Energy-based Out-of-Distribution Detection for Graph Neural Networks. ICLR (2023)
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. CoRR (2023)
Chenxiao Yang, Qitian Wu, Jiahua Wang, Junchi Yan
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. ICLR (2023)
Tianqi Zhang, Qitian Wu, Junchi Yan
Learning High-Order Graph Convolutional Networks via Adaptive Layerwise Aggregation Combination. IEEE Trans. Neural Networks Learn. Syst. 34 (8) (2023)
Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan
How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space. CoRR (2023)
Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan
Energy-based Out-of-Distribution Detection for Graph Neural Networks. CoRR (2023)
Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan
Simplifying and Empowering Transformers for Large-Graph Representations. NeurIPS (2023)
Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, Junchi Yan
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. CoRR (2023)
Nianzu Yang, Kaipeng Zeng, Qitian Wu, Junchi Yan
MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning. WWW (2023)
Zenan Li, Qitian Wu, Fan Nie, Junchi Yan
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs. NeurIPS (2022)
Chenxiao Yang, Qitian Wu, Junchi Yan
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. NeurIPS (2022)
Chenxiao Yang, Qitian Wu, Jiahua Wang, Junchi Yan
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. CoRR (2022)
Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. NeurIPS (2022)
Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, Junchi Yan
Learning Substructure Invariance for Out-of-Distribution Molecular Representations. NeurIPS (2022)
Chenxiao Yang, Qitian Wu, Jipeng Jin, Xiaofeng Gao, Junwei Pan, Guihai Chen
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach. CoRR (2022)
Qibing Ren, Yiting Chen, Yichuan Mo, Qitian Wu, Junchi Yan
DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness. KDD (2022)
Chenxiao Yang, Qitian Wu, Jipeng Jin, Xiaofeng Gao, Junwei Pan, Guihai Chen
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach. IJCAI (2022)
Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. NeurIPS (2022)
Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf
Towards Distribution Shift of Node-Level Prediction on Graphs: An Invariance Perspective. CoRR (2022)
Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf
Handling Distribution Shifts on Graphs: An Invariance Perspective. ICLR (2022)
Danning Lao, Xinyu Yang, Qitian Wu, Junchi Yan
Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation. KDD (2022)
Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. CoRR (2022)
Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu
Localized Contrastive Learning on Graphs. CoRR (2022)
Chenxiao Yang, Qitian Wu, Junchi Yan
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. CoRR (2022)
Qitian Wu, Chenxiao Yang, Junchi Yan
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. NeurIPS (2021)
Qitian Wu, Chenxiao Yang, Shuodian Yu, Xiaofeng Gao, Guihai Chen
Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders. CIKM (2021)
Qitian Wu, Rui Gao, Hongyuan Zha
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators. NeurIPS (2021)
Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. NeurIPS (2021)
Qitian Wu, Chenxiao Yang, Junchi Yan
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. CoRR (2021)
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach. ICML (2021)
Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. CoRR (2021)
Qitian Wu, Hengrui Zhang, Hongyuan Zha
Inductive Relational Matrix Completion. CoRR (2020)
Xiaosong Jia, Qitian Wu, Xiaofeng Gao, Guihai Chen
SentiMem: Attentive Memory Networks for Sentiment Classification in User Review. DASFAA (1) (2020)
Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. CoRR (2019)
Qitian Wu, Rui Gao, Hongyuan Zha
Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models. CoRR (2019)
Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. NeurIPS (2019)
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. CoRR (2019)
Qitian Wu, Yirui Gao, Xiaofeng Gao, Paul Weng, Guihai Chen
Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination. KDD (2019)
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. WWW (2019)
Qitian Wu, Lei Jiang, Xiaofeng Gao, Xiaochun Yang, Guihai Chen
Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. IJCAI (2019)
Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, Guihai Chen
Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction. CIKM (2018)
Qitian Wu, Chaoqi Yang, Xiaofeng Gao, Peng He, Guihai Chen
EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction. ICDM (2018)
Chaoqi Yang, Qitian Wu, Xiaofeng Gao, Guihai Chen
EPOC: A Survival Perspective Early Pattern Detection Model for Outbreak Cascades. DEXA (1) (2018)