| How Powerful are Graph Neural Networks? K Xu*, W Hu*, J Leskovec, S Jegelka International Conference on Learning Representations, 2019 | 1096 | 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 | 355 | 2018 |
| 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 | 170 | 2017 |
| 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 | 96 | 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 | 84 | 2020 |
| Does Distributionally Robust Supervised Learning Give Robust Classifiers? W Hu, G Niu, I Sato, M Sugiyama International Conference on Machine Learning, 2018 | 56 | 2018 |
| Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama Advances in Neural Information Processing Systems, 2017 | 47 | 2017 |
| 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 | 30 | 2016 |
| Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings H Ren*, W Hu*, J Leskovec International Conference on Learning Representations, 2020 | 20 | 2020 |
| 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 | 14 | 2017 |
| WILDS: A Benchmark of in-the-Wild Distribution Shifts PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ... arXiv preprint arXiv:2012.07421, 2020 | 4 | 2020 |
| The Open Catalyst 2020 (OC20) Dataset and Community Challenges L Chanussot, A Das, S Goyal, T Lavril, M Shuaibi, M Riviere, K Tran, ... arXiv preprint arXiv:2010.09990, 2020 | 4 | 2020 |
| 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 | 1 | 2020 |
| Tight upper bounds on the redundancy of optimal binary AIFV codes W Hu, H Yamamoto, J Honda IEEE International Symposium on Information Theory, 6-10, 2016 | 1 | 2016 |
| 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 | | 2021 |
| 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 | | 2019 |
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
