| Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems Q Wu, H Zhang, X Gao, P He, P Weng, H Gao, G Chen The world wide web conference, 2091-2102, 2019 | 378 | 2019 |
| From canonical correlation analysis to self-supervised graph neural networks H Zhang, Q Wu, J Yan, D Wipf, PS Yu Advances in Neural Information Processing Systems 34, 76-89, 2021 | 252 | 2021 |
| Handling distribution shifts on graphs: An invariance perspective Q Wu, H Zhang, J Yan, D Wipf arXiv preprint arXiv:2202.02466, 2022 | 216 | 2022 |
| Simplifying and empowering transformers for large-graph representations Q Wu, W Zhao, C Yang, H Zhang, F Nie, H Jiang, Y Bian, J Yan Advances in Neural Information Processing Systems 36, 2024 | 87 | 2024 |
| Mixed-type tabular data synthesis with score-based diffusion in latent space H Zhang, J Zhang, B Srinivasan, Z Shen, X Qin, C Faloutsos, H Rangwala, ... International Conference on Learning Representations, 2023 | 57 | 2023 |
| Towards open-world recommendation: An inductive model-based collaborative filtering approach Q Wu, H Zhang, X Gao, J Yan, H Zha International Conference on Machine Learning, 11329-11339, 2021 | 54 | 2021 |
| Contrastvae: Contrastive variational autoencoder for sequential recommendation Y Wang, H Zhang, Z Liu, L Yang, PS Yu Proceedings of the 31st ACM International Conference on Information …, 2022 | 53 | 2022 |
| M-mix: Generating hard negatives via multi-sample mixing for contrastive learning S Zhang, M Liu, J Yan, H Zhang, L Huang, X Yang, P Lu Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 52 | 2022 |
| Align representations with base: A new approach to self-supervised learning S Zhang, L Qiu, F Zhu, J Yan, H Zhang, R Zhao, H Li, X Yang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 29 | 2022 |
| Adversarial training model unifying feature driven and point process perspectives for event popularity prediction Q Wu, C Yang, H Zhang, X Gao, P Weng, G Chen Proceedings of the 27th ACM International conference on information and …, 2018 | 25 | 2018 |
| Stacked mixed-order graph convolutional networks for collaborative filtering H Zhang, J McAuley Proceedings of the 2020 SIAM International Conference on Data Mining, 73-81, 2020 | 24 | 2020 |
| Localized contrastive learning on graphs H Zhang, Q Wu, Y Wang, S Zhang, J Yan, PS Yu arXiv preprint arXiv:2212.04604, 2022 | 18 | 2022 |
| Orthoreg: Improving graph-regularized mlps via orthogonality regularization H Zhang, S Wang, VN Ioannidis, S Adeshina, J Zhang, X Qin, C Faloutsos, ... arXiv preprint arXiv:2302.00109, 2023 | 5 | 2023 |
| Unleashing the Potential of Diffusion Models for Incomplete Data Imputation H Zhang, L Fang, PS Yu arXiv preprint arXiv:2405.20690, 2024 | 2 | 2024 |
| Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data H Zhang, L Fang, Q Wu, PS Yu arXiv preprint arXiv:2410.21523, 2024 | | 2024 |
| InfoMLP: Unlocking the Potential of MLPs for Semi-Supervised Learning with Structured Data H Zhang, Q Wu, C Yang, PS Yu Proceedings of the 33rd ACM International Conference on Information and …, 2024 | | 2024 |
| Revisit Orthogonality in Graph-Regularized MLPs H Zhang, S Wang, VN Ioannidis, S Adeshina, J Zhang, X Qin, C Faloutsos, ... Proceedings of the 33rd ACM International Conference on Information and …, 2024 | | 2024 |
| SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity Q Wu, K Yang, H Zhang, D Wipf, J Yan arXiv preprint arXiv:2409.09007, 2024 | | 2024 |
| TabDiff: a Unified Diffusion Model for Multi-Modal Tabular Data Generation J Shi, M Xu, H Hua, H Zhang, S Ermon, J Leskovec NeurIPS 2024 Third Table Representation Learning Workshop, 0 | | |
Qitian WuBroad Institute of MIT and HarvardBestätigte E-Mail-Adresse bei broadinstitute.org
