| Marginalized kernels between labeled graphs H Kashima, K Tsuda, A Inokuchi Proceedings of the 20th international conference on machine learning (ICML …, 2003 | 1215 | 2003 |
| Direct importance estimation with model selection and its application to covariate shift adaptation M Sugiyama, S Nakajima, H Kashima, P Buenau, M Kawanabe Advances in neural information processing systems 20, 2007 | 1196 | 2007 |
| Direct importance estimation for covariate shift adaptation M Sugiyama, T Suzuki, S Nakajima, H Kashima, P Von Bünau, ... Annals of the Institute of Statistical Mathematics 60 (4), 699-746, 2008 | 585 | 2008 |
| Roughly balanced bagging for imbalanced data S Hido, H Kashima, Y Takahashi Statistical Analysis and Data Mining: The ASA Data Science Journal 2 (5‐6 …, 2009 | 376 | 2009 |
| Eigenspace-based anomaly detection in computer systems T Idé, H Kashima Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 344 | 2004 |
| Nonparametric return distribution approximation for reinforcement learning T Morimura, M Sugiyama, H Kashima, H Hachiya, T Tanaka Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 316 | 2010 |
| Random features strengthen graph neural networks R Sato, M Yamada, H Kashima Proceedings of the 2021 SIAM international conference on data mining (SDM …, 2021 | 303 | 2021 |
| Knowledge tracing machines: Factorization machines for knowledge tracing JJ Vie, H Kashima Proceedings of the AAAI conference on artificial intelligence 33 (01), 750-757, 2019 | 278 | 2019 |
| Statistical outlier detection using direct density ratio estimation S Hido, Y Tsuboi, H Kashima, M Sugiyama, T Kanamori Knowledge and information systems 26 (2), 309-336, 2011 | 263 | 2011 |
| Estimation of low-rank tensors via convex optimization R Tomioka, K Hayashi, H Kashima arXiv preprint arXiv:1010.0789, 2010 | 234 | 2010 |
| Tensor factorization using auxiliary information A Narita, K Hayashi, R Tomioka, H Kashima Data Mining and Knowledge Discovery 25 (2), 298-324, 2012 | 213 | 2012 |
| A parameterized probabilistic model of network evolution for supervised link prediction H Kashima, N Abe Sixth International Conference on Data Mining (ICDM'06), 340-349, 2006 | 211 | 2006 |
| Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in neural information processing systems 24, 2011 | 197 | 2011 |
| Link propagation: A fast semi-supervised learning algorithm for link prediction H Kashima, T Kato, Y Yamanishi, M Sugiyama, K Tsuda Proceedings of the 2009 SIAM international conference on data mining, 1100-1111, 2009 | 191 | 2009 |
| Direct density ratio estimation for large-scale covariate shift adaptation Y Tsuboi, H Kashima, S Hido, S Bickel, M Sugiyama Journal of Information Processing 17, 138-155, 2009 | 191 | 2009 |
| A linear-time graph kernel S Hido, H Kashima 2009 Ninth IEEE International Conference on Data Mining, 179-188, 2009 | 188 | 2009 |
| Kernels for semi-structured data H Kashima, T Koyanagi Proceedings of the 19th international conference on machine learning (ICML …, 2002 | 179 | 2002 |
| Kernels for graphs H Kashima, K Tsuda, A Inokuchi Kernel methods in computational biology, 155-170, 2004 | 178 | 2004 |
| Approximation ratios of graph neural networks for combinatorial problems R Sato, M Yamada, H Kashima Advances in Neural Information Processing Systems 32, 2019 | 165 | 2019 |
| Parametric return density estimation for reinforcement learning T Morimura, M Sugiyama, H Kashima, H Hachiya, T Tanaka arXiv preprint arXiv:1203.3497, 2012 | 165 | 2012 |
Yukino BabaAssociate Professor, The University of TokyoVerified email at g.ecc.u-tokyo.ac.jp
