| How Powerful are Graph Neural Networks? K Xu*, W Hu*, J Leskovec, S Jegelka International Conference on Learning Representations, 2019 | 4812 | 2019 |
| Co-teaching: robust training deep neural networks with extremely noisy labels B Han, Q Yao, X Yu, G Niu, M Xu, W Hu, I Tsang, M Sugiyama Advances in Neural Information Processing Systems, 2018 | 1446 | 2018 |
| Open Graph Benchmark: Datasets for Machine Learning on Graphs W Hu, M Fey, M Zitnik, Y Dong, H Ren, B Liu, M Catasta, J Leskovec Advances in Neural Information Processing Systems, 2020 | 1323 | 2020 |
| Strategies for Pre-training Graph Neural Networks W Hu*, B Liu*, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec International Conference on Learning Representations, 2020 | 800 | 2020 |
| Wilds: A benchmark of in-the-wild distribution shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... International Conference on Machine Learning, 5637-5664, 2021 | 703 | 2021 |
| Learning Discrete Representations via Information Maximizing Self-Augmented Training W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama International Conference on Machine Learning, 2017 | 407 | 2017 |
| Does Distributionally Robust Supervised Learning Give Robust Classifiers? W Hu, G Niu, I Sato, M Sugiyama International Conference on Machine Learning, 2018 | 214 | 2018 |
| Open catalyst 2020 (OC20) dataset and community challenges L Chanussot, A Das, S Goyal, T Lavril, M Shuaibi, M Riviere, K Tran, ... Acs Catalysis 11 (10), 6059-6072, 2021 | 197 | 2021 |
| Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings H Ren*, W Hu*, J Leskovec International Conference on Learning Representations, 2020 | 185 | 2020 |
| OGB-LSC: A large-scale challenge for machine learning on graphs W Hu, M Fey, H Ren, M Nakata, Y Dong, J Leskovec Advances in Neural Information Processing Systems, 2021 | 161 | 2021 |
| Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama Advances in Neural Information Processing Systems, 2017 | 129 | 2017 |
| Extending the wilds benchmark for unsupervised adaptation S Sagawa, PW Koh, T Lee, I Gao, SM Xie, K Shen, A Kumar, W Hu, ... International Conference on Learning Representations, 2021 | 45 | 2021 |
| A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings W Hu, J Tsujii The Annual Meeting of the Association for Computational Linguistics, 380-386, 2016 | 45 | 2016 |
| An introduction to electrocatalyst design using machine learning for renewable energy storage CL Zitnick, L Chanussot, A Das, S Goyal, J Heras-Domingo, C Ho, W Hu, ... arXiv preprint arXiv:2010.09435, 2020 | 40 | 2020 |
| Forcenet: A graph neural network for large-scale quantum calculations W Hu, M Shuaibi, A Das, S Goyal, A Sriram, J Leskovec, D Parikh, ... arXiv preprint arXiv:2103.01436, 2021 | 30 | 2021 |
| GraphCast: Learning skillful medium-range global weather forecasting R Lam, A Sanchez-Gonzalez, M Willson, P Wirnsberger, M Fortunato, ... arXiv preprint arXiv:2212.12794, 2022 | 22 | 2022 |
| Worst-case redundancy of optimal binary AIFV codes and their extended codes W Hu, H Yamamoto, J Honda IEEE Transactions on Information Theory 63 (8), 5074-5086, 2017 | 20 | 2017 |
| Learning Backward Compatible Embeddings W Hu, R Bansal, K Cao, N Rao, K Subbian, J Leskovec ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022 | 9 | 2022 |
| Unsupervised Discrete Representation Learning W Hu, T Miyato, S Tokui, E Matsumoto, M Sugiyama Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 97-119, 2019 | 3 | 2019 |
| TuneUp: A Training Strategy for Improving Generalization of Graph Neural Networks W Hu, K Cao, K Huang, EW Huang, K Subbian, J Leskovec arXiv preprint arXiv:2210.14843, 2022 | 2 | 2022 |
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
