| A survey on the expressive power of graph neural networks R Sato arXiv preprint arXiv:2003.04078, 2020 | 66 | 2020 |
| Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019 | 62 | 2019 |
| Random features strengthen graph neural networks R Sato, M Yamada, H Kashima Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 52 | 2021 |
| Short-term precipitation prediction with skip-connected prednet R Sato, H Kashima, T Yamamoto International Conference on Artificial Neural Networks, 373-382, 2018 | 18 | 2018 |
| Fast unbalanced optimal transport on a tree R Sato, M Yamada, H Kashima Advances in neural information processing systems 33, 19039-19051, 2020 | 12 | 2020 |
| Constant time graph neural networks R Sato, M Yamada, H Kashima ACM Transactions on Knowledge Discovery from Data (TKDD) 16 (5), 1-31, 2022 | 6 | 2022 |
| Fast and robust comparison of probability measures in heterogeneous spaces R Sato, M Cuturi, M Yamada, H Kashima arXiv preprint arXiv:2002.01615, 2020 | 6 | 2020 |
| Learning to Sample Hard Instances for Graph Algorithms R Sato, M Yamada, H Kashima Asian Conference on Machine Learning, 503--518, 2019 | 3* | 2019 |
| Fixed Support Tree-Sliced Wasserstein Barycenter Y Takezawa, R Sato, Z Kozareva, S Ravi, M Yamada arXiv preprint arXiv:2109.03431, 2021 | 2 | 2021 |
| Supervised tree-wasserstein distance Y Takezawa, R Sato, M Yamada International Conference on Machine Learning, 10086-10095, 2021 | 2 | 2021 |
| Re-evaluating Word Mover's Distance R Sato, M Yamada, H Kashima arXiv preprint arXiv:2105.14403, 2021 | 2 | 2021 |
| Feature robust optimal transport for high-dimensional data M Petrovich, C Liang, R Sato, Y Liu, YHH Tsai, L Zhu, Y Yang, ... arXiv preprint arXiv:2005.12123, 2020 | 2 | 2020 |
| Poincare: Recommending publication venues via treatment effect estimation R Sato, M Yamada, H Kashima Journal of Informetrics 16 (2), 101283, 2022 | 1 | 2022 |
| Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? R Sato Proceedings of the 2022 SIAM International Conference on Data Mining (SDM …, 2022 | 1 | 2022 |
| Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem R Sato arXiv preprint arXiv:2205.01954, 2022 | | 2022 |
| Enumerating Fair Packages for Group Recommendations R Sato Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022 | | 2022 |
| Retrieving Black-box Optimal Images from External Databases R Sato arXiv preprint arXiv:2112.14921, 2021 | | 2021 |
Makoto YamadaKyoto University / RIKEN Center for Advanced Intelligence Project (AIP)Verified email at i.kyoto-u.ac.jp
