| Estimation of low-rank tensors via convex optimization R Tomioka, K Hayashi, H Kashima arXiv preprint arXiv:1010.0789, 2010 | 194 | 2010 |
| Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in Neural Information Processing Systems (NIPS), 137, 2011 | 162 | 2011 |
| Tensor factorization using auxiliary information A Narita, K Hayashi, R Tomioka, H Kashima Data Mining and Knowledge Discovery 25 (2), 298-324, 2012 | 133 | 2012 |
| Making tree ensembles interpretable S Hara, K Hayashi arXiv preprint arXiv:1606.05390, 2016 | 59 | 2016 |
| Cross-temporal link prediction S Oyama, K Hayashi, H Kashima 2011 IEEE 11th International Conference on Data Mining, 1188-1193, 2011 | 49 | 2011 |
| Making tree ensembles interpretable: A bayesian model selection approach S Hara, K Hayashi International conference on artificial intelligence and statistics, 77-85, 2018 | 41 | 2018 |
| On the extension of trace norm to tensors R Tomioka, K Hayashi, H Kashima NIPS Workshop on Tensors, Kernels, and Machine Learning 7, 2010 | 37 | 2010 |
| Factorized asymptotic Bayesian hidden Markov models R Fujimaki, K Hayashi arXiv preprint arXiv:1206.4679, 2012 | 28 | 2012 |
| Expected tensor decomposition with stochastic gradient descent T Maehara, K Hayashi, K Kawarabayashi Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 27 | 2016 |
| Real-time top-R topic detection on twitter with topic hijack filtering K Hayashi, T Maehara, M Toyoda, K Kawarabayashi Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 26 | 2015 |
| Factorized asymptotic bayesian inference for latent feature models K Hayashi, R Fujimaki Advances in Neural Information Processing Systems, 1214-1222, 2013 | 26 | 2013 |
| Exponential family tensor factorization for missing-values prediction and anomaly detection K Hayashi, T Takenouchi, T Shibata, Y Kamiya, D Kato, K Kunieda, ... 2010 IEEE International Conference on Data Mining, 216-225, 2010 | 22 | 2010 |
| Self-measuring similarity for multi-task gaussian process K Hayashi, T Takenouchi, R Tomioka, H Kashima Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 145-153, 2012 | 21 | 2012 |
| Doubly decomposing nonparametric tensor regression M Imaizumi, K Hayashi International Conference on Machine Learning, 727-736, 2016 | 18 | 2016 |
| On tensor train rank minimization: Statistical efficiency and scalable algorithm M Imaizumi, T Maehara, K Hayashi Advances in Neural Information Processing Systems, 3930-3939, 2017 | 15 | 2017 |
| A tractable fully bayesian method for the stochastic block model K Hayashi, T Konishi, T Kawamoto arXiv preprint arXiv:1602.02256, 2016 | 15 | 2016 |
| Rebuilding factorized information criterion: Asymptotically accurate marginal likelihood K Hayashi, S Maeda, R Fujimaki International Conference on Machine Learning, 1358-1366, 2015 | 15 | 2015 |
| Dynamic exponential family matrix factorization K Hayashi, JI Hirayama, S Ishii Pacific-Asia Conference on Knowledge Discovery and Data Mining, 452-462, 2009 | 14 | 2009 |
| Exploring unexplored tensor network decompositions for convolutional neural networks K Hayashi, T Yamaguchi, Y Sugawara, S Maeda Advances in Neural Information Processing Systems, 5552-5562, 2019 | 11 | 2019 |
| When Does Label Propagation Fail? A View from a Network Generative Model Y Yamaguchi, K Hayashi IJCAI, 2017 | 9 | 2017 |
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
