| 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 33, 22118-22133, 2020 | 1081 | 2020 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 686 | 2021 |
| QA-GNN: Reasoning with language models and knowledge graphs for question answering M Yasunaga, H Ren, A Bosselut, P Liang, J Leskovec arXiv preprint arXiv:2104.06378, 2021 | 161 | 2021 |
| Query2box: Reasoning over knowledge graphs in vector space using box embeddings H Ren, W Hu, J Leskovec arXiv preprint arXiv:2002.05969, 2020 | 154 | 2020 |
| Multi-agent generative adversarial imitation learning J Song, H Ren, D Sadigh, S Ermon Advances in neural information processing systems 31, 2018 | 153 | 2018 |
| Ogb-lsc: A large-scale challenge for machine learning on graphs W Hu, M Fey, H Ren, M Nakata, Y Dong, J Leskovec arXiv preprint arXiv:2103.09430, 2021 | 124 | 2021 |
| Bias and generalization in deep generative models: An empirical study S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon Advances in Neural Information Processing Systems 31, 2018 | 87 | 2018 |
| Beta embeddings for multi-hop logical reasoning in knowledge graphs H Ren, J Leskovec Advances in Neural Information Processing Systems 33, 19716-19726, 2020 | 83 | 2020 |
| Graph information bottleneck T Wu, H Ren, P Li, J Leskovec Advances in Neural Information Processing Systems 33, 20437-20448, 2020 | 81 | 2020 |
| Relational message passing for knowledge graph completion H Wang, H Ren, J Leskovec Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 72* | 2021 |
| RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment H Ren, D Chen, Y Wang AAAI'18, 2017 | 53 | 2017 |
| Lego: Latent execution-guided reasoning for multi-hop question answering on knowledge graphs H Ren, H Dai, B Dai, X Chen, M Yasunaga, H Sun, D Schuurmans, ... International conference on machine learning, 8959-8970, 2021 | 36 | 2021 |
| Greaselm: Graph reasoning enhanced language models X Zhang, A Bosselut, M Yasunaga, H Ren, P Liang, CD Manning, ... International conference on learning representations, 2022 | 30* | 2022 |
| Learning with weak supervision from physics and data-driven constraints H Ren, R Stewart, J Song, V Kuleshov, S Ermon AI Magazine 39 (1), 27-38, 2018 | 29 | 2018 |
| No-reference image quality assessment: An attention driven approach D Chen, Y Wang, W Gao IEEE Transactions on Image Processing 29, 6496-6506, 2020 | 27 | 2020 |
| Combiner: Full attention transformer with sparse computation cost H Ren, H Dai, Z Dai, M Yang, J Leskovec, D Schuurmans, B Dai Advances in Neural Information Processing Systems 34, 22470-22482, 2021 | 24 | 2021 |
| Adaptive antithetic sampling for variance reduction H Ren, S Zhao, S Ermon International Conference on Machine Learning, 5420-5428, 2019 | 16 | 2019 |
| Modeling heterogeneous hierarchies with relation-specific hyperbolic cones Y Bai, Z Ying, H Ren, J Leskovec Advances in Neural Information Processing Systems 34, 12316-12327, 2021 | 14 | 2021 |
| Adversarial Constraint Learning for Structured Prediction H Ren, R Stewart, J Song, V Kuleshov, S Ermon IJCAI'18, 2018 | 14 | 2018 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 12 | 2022 |
Jure LeskovecProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
