Qitian Wu

According to our database, Qitian Wu authored at least 40 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation.

Tianqi Zhang, Qitian Wu, Junchi Yan, Yunan Zhao, Bing Han

IEEE Trans. Neural Networks Learn. Syst., April, 2024

Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions.

Wujiang Xu, Qitian Wu, Runzhong Wang, Mingming Ha, Qiongxu Ma, Linxun Chen, Bing Han, Junchi Yan

Proceedings of the ACM on Web Conference 2024, 2024

Graph Out-of-Distribution Generalization via Causal Intervention.

Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan

Proceedings of the ACM on Web Conference 2024, 2024

TDeLTA: A Light-Weight and Robust Table Detection Method Based on Learning Text Arrangement.

Yang Fan, Xiangping Wu, Qingcai Chen, Heng Li, Yan Huang, Zhixiang Cai, Qitian Wu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Learning High-Order Graph Convolutional Networks via Adaptive Layerwise Aggregation Combination.

Tianqi Zhang, Qitian Wu, Junchi Yan

IEEE Trans. Neural Networks Learn. Syst., August, 2023

Advective Diffusion Transformers for Topological Generalization in Graph Learning.

Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Fan Nie, Michael Bronstein, Junchi Yan

CoRR, 2023

How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space.

Chenxiao Yang, Qitian Wu, David Wipf, Ruoyu Sun, Junchi Yan

CoRR, 2023

MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning.

Nianzu Yang, Kaipeng Zeng, Qitian Wu, Junchi Yan

Proceedings of the ACM Web Conference 2023, 2023

Simplifying and Empowering Transformers for Large-Graph Representations.

Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift.

Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks.

Wentao Zhao, Qitian Wu, Chenxiao Yang, Junchi Yan

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs.

Chenxiao Yang, Qitian Wu, Jiahua Wang, Junchi Yan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion.

Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, Junchi Yan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Energy-based Out-of-Distribution Detection for Graph Neural Networks.

Qitian Wu, Yiting Chen, Chenxiao Yang, Junchi Yan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Localized Contrastive Learning on Graphs.

Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu

CoRR, 2022

Towards Distribution Shift of Node-Level Prediction on Graphs: An Invariance Perspective.

Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf

CoRR, 2022

Learning Substructure Invariance for Out-of-Distribution Molecular Representations.

Nianzu Yang, Kaipeng Zeng, Qitian Wu, Xiaosong Jia, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks.

Chenxiao Yang, Qitian Wu, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment.

Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification.

Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs.

Zenan Li, Qitian Wu, Fan Nie, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness.

Qibing Ren, Yiting Chen, Yichuan Mo, Qitian Wu, Junchi Yan

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Variational Inference for Training Graph Neural Networks in Low-Data Regime through Joint Structure-Label Estimation.

Danning Lao, Xinyu Yang, Qitian Wu, Junchi Yan

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach.

Chenxiao Yang, Qitian Wu, Jipeng Jin, Xiaofeng Gao, Junwei Pan, Guihai Chen

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Handling Distribution Shifts on Graphs: An Invariance Perspective.

Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach.

Qitian Wu, Chenxiao Yang, Junchi Yan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators.

Qitian Wu, Rui Gao, Hongyuan Zha

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach.

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha

Proceedings of the 38th International Conference on Machine Learning, 2021

Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders.

Qitian Wu, Chenxiao Yang, Shuodian Yu, Xiaofeng Gao, Guihai Chen

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

Inductive Relational Matrix Completion.

Qitian Wu, Hengrui Zhang, Hongyuan Zha

CoRR, 2020

SentiMem: Attentive Memory Networks for Sentiment Classification in User Review.

Xiaosong Jia, Qitian Wu, Xiaofeng Gao, Guihai Chen

Proceedings of the Database Systems for Advanced Applications, 2020

2019

Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models.

Qitian Wu, Rui Gao, Hongyuan Zha

CoRR, 2019

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems.

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen

Proceedings of the World Wide Web Conference, 2019

Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling.

Qitian Wu, Zixuan Zhang, Xiaofeng Gao, Junchi Yan, Guihai Chen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination.

Qitian Wu, Yirui Gao, Xiaofeng Gao, Paul Weng, Guihai Chen

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust.

Qitian Wu, Lei Jiang, Xiaofeng Gao, Xiaochun Yang, Guihai Chen

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

EPAB: Early Pattern Aware Bayesian Model for Social Content Popularity Prediction.

Qitian Wu, Chaoqi Yang, Xiaofeng Gao, Peng He, Guihai Chen

Proceedings of the IEEE International Conference on Data Mining, 2018

EPOC: A Survival Perspective Early Pattern Detection Model for Outbreak Cascades.

Chaoqi Yang, Qitian Wu, Xiaofeng Gao, Guihai Chen

Proceedings of the Database and Expert Systems Applications, 2018

Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction.

Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, Guihai Chen

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018