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2020 – today
- 2024
[j2]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): 4478-4490 (2024)- [c37]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: 1670-1678 - [c36]Hengrui Zhang, Qitian Wu, Chenxiao Yang, Philip S. Yu:
InfoMLP: Unlocking the Potential of MLPs for Semi-Supervised Learning with Structured Data. CIKM 2024: 3155-3164 - [c35]Tianyi Bao, Qitian Wu, Zetian Jiang, Yiting Chen, Jiawei Sun, Junchi Yan:
Graph Out-of-Distribution Detection Goes Neighborhood Shaping. ICML 2024 - [c34]Qitian Wu, Fan Nie, Chenxiao Yang, Junchi Yan:
Learning Divergence Fields for Shift-Robust Graph Representations. ICML 2024 - [c33]Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan:
How Graph Neural Networks Learn: Lessons from Training Dynamics. ICML 2024 - [c32]Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan:
GeoMix: Towards Geometry-Aware Data Augmentation. KDD 2024: 4500-4511 - [c31]Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan:
Graph Out-of-Distribution Generalization via Causal Intervention. WWW 2024: 850-860 - [c30]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: 3173-3184 - [i27]Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan:
Graph Out-of-Distribution Generalization via Causal Intervention. CoRR abs/2402.11494 (2024) - [i26]Qitian Wu, Fan Nie, Chenxiao Yang, Junchi Yan:
Learning Divergence Fields for Shift-Robust Graph Representations. CoRR abs/2406.04963 (2024) - [i25]Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan:
GeoMix: Towards Geometry-Aware Data Augmentation. CoRR abs/2407.10681 (2024) - [i24]Qitian Wu, Kai Yang, Hengrui Zhang, David Wipf, Junchi Yan:
SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity. CoRR abs/2409.09007 (2024) - [i23]Qitian Wu, David Wipf, Junchi Yan:
Neural Message Passing Induced by Energy-Constrained Diffusion. CoRR abs/2409.09111 (2024) - [i22]Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu:
Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data. CoRR abs/2410.21523 (2024) - 2023
- [j1]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): 5144-5155 (2023) - [c29]Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan:
Energy-based Out-of-Distribution Detection for Graph Neural Networks. ICLR 2023 - [c28]Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan:
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. ICLR 2023 - [c27]Chenxiao Yang, Qitian Wu, Jiahua Wang, Junchi Yan:
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. ICLR 2023 - [c26]Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan:
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks. KDD 2023: 3525-3536 - [c25]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 - [c24]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 - [c23]Nianzu Yang, Kaipeng Zeng, Qitian Wu, Junchi Yan:
MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning. WWW 2023: 4075-4085 - [i21]Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan:
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion. CoRR abs/2301.09474 (2023) - [i20]Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan:
Energy-based Out-of-Distribution Detection for Graph Neural Networks. CoRR abs/2302.02914 (2023) - [i19]Qitian Wu, Wentao Zhao, Zenan Li, David Wipf, Junchi Yan:
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. CoRR abs/2306.08385 (2023) - [i18]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 abs/2306.10759 (2023) - [i17]Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan:
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks. CoRR abs/2306.11264 (2023) - [i16]Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan:
How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space. CoRR abs/2310.05105 (2023) - [i15]Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Fan Nie, Michael M. Bronstein, Junchi Yan:
Advective Diffusion Transformers for Topological Generalization in Graph Learning. CoRR abs/2310.06417 (2023) - [i14]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 abs/2311.04590 (2023) - [i13]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 abs/2312.11043 (2023) - 2022
- [c22]Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf:
Handling Distribution Shifts on Graphs: An Invariance Perspective. ICLR 2022 - [c21]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: 2355-2361 - [c20]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: 824-834 - [c19]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: 1483-1492 - [c18]Zenan Li, Qitian Wu, Fan Nie, Junchi Yan:
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs. NeurIPS 2022 - [c17]Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan:
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. NeurIPS 2022 - [c16]Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan:
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. NeurIPS 2022 - [c15]Chenxiao Yang, Qitian Wu, Junchi Yan:
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. NeurIPS 2022 - [c14]Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, Junchi Yan:
Learning Substructure Invariance for Out-of-Distribution Molecular Representations. NeurIPS 2022 - [i12]Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf:
Towards Distribution Shift of Node-Level Prediction on Graphs: An Invariance Perspective. CoRR abs/2202.02466 (2022) - [i11]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 abs/2204.11752 (2022) - [i10]Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan:
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment. CoRR abs/2210.13005 (2022) - [i9]Chenxiao Yang, Qitian Wu, Junchi Yan:
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks. CoRR abs/2210.13014 (2022) - [i8]Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu:
Localized Contrastive Learning on Graphs. CoRR abs/2212.04604 (2022) - [i7]Chenxiao Yang, Qitian Wu, Jiahua Wang, Junchi Yan:
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs. CoRR abs/2212.09034 (2022) - 2021
- [c13]Qitian Wu, Chenxiao Yang, Shuodian Yu, Xiaofeng Gao, Guihai Chen:
Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders. CIKM 2021: 2160-2169 - [c12]Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha:
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach. ICML 2021: 11329-11339 - [c11]Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu:
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. NeurIPS 2021: 76-89 - [c10]Qitian Wu, Rui Gao, Hongyuan Zha:
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators. NeurIPS 2021: 11274-11286 - [c9]Qitian Wu, Chenxiao Yang, Junchi Yan:
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. NeurIPS 2021: 19435-19447 - [i6]Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu:
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. CoRR abs/2106.12484 (2021) - [i5]Qitian Wu, Chenxiao Yang, Junchi Yan:
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach. CoRR abs/2110.04514 (2021) - 2020
- [c8]Xiaosong Jia, Qitian Wu, Xiaofeng Gao, Guihai Chen:
SentiMem: Attentive Memory Networks for Sentiment Classification in User Review. DASFAA (1) 2020: 736-751 - [i4]Qitian Wu, Hengrui Zhang, Hongyuan Zha:
Inductive Relational Matrix Completion. CoRR abs/2007.04833 (2020)
2010 – 2019
- 2019
- [c7]Qitian Wu, Lei Jiang, Xiaofeng Gao, Xiaochun Yang, Guihai Chen:
Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. IJCAI 2019: 3877-3883 - [c6]Qitian Wu, Yirui Gao, Xiaofeng Gao, Paul Weng, Guihai Chen:
Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination. KDD 2019: 447-457 - [c5]Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen:
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. NeurIPS 2019: 3842-3851 - [c4]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: 2091-2102 - [i3]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 abs/1903.10433 (2019) - [i2]Qitian Wu, Rui Gao, Hongyuan Zha:
Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models. CoRR abs/1909.13035 (2019) - [i1]Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen:
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling. CoRR abs/1910.12469 (2019) - 2018
- [c3]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: 517-526 - [c2]Chaoqi Yang, Qitian Wu, Xiaofeng Gao, Guihai Chen:
EPOC: A Survival Perspective Early Pattern Detection Model for Outbreak Cascades. DEXA (1) 2018: 336-351 - [c1]Qitian Wu, Chaoqi Yang, Xiaofeng Gao, Peng He, Guihai Chen:
EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction. ICDM 2018: 1296-1301
Coauthor Index
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last updated on 2024-12-02 21:25 CET by the dblp team
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