| Making tree ensembles interpretable: A bayesian model selection approach S Hara, K Hayashi International conference on artificial intelligence and statistics, 77-85, 2018 | 253 | 2018 |
| Estimation of low-rank tensors via convex optimization R Tomioka, K Hayashi, H Kashima arXiv preprint arXiv:1010.0789, 2010 | 232 | 2010 |
| Tensor factorization using auxiliary information A Narita, K Hayashi, R Tomioka, H Kashima Data Mining and Knowledge Discovery 25, 298-324, 2012 | 203 | 2012 |
| Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in Neural Information Processing Systems (NIPS), 137, 2011 | 188 | 2011 |
| Exploring unexplored tensor network decompositions for convolutional neural networks K Hayashi, T Yamaguchi, Y Sugawara, S Maeda Advances in Neural Information Processing Systems 32, 2019 | 79 | 2019 |
| Cross-temporal link prediction S Oyama, K Hayashi, H Kashima 2011 IEEE 11th International Conference on Data Mining, 1188-1193, 2011 | 57 | 2011 |
| Expected tensor decomposition with stochastic gradient descent T Maehara, K Hayashi, K Kawarabayashi Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 53 | 2016 |
| On the extension of trace norm to tensors R Tomioka, K Hayashi, H Kashima NIPS workshop on tensors, kernels, and machine learning 7, 2010 | 42 | 2010 |
| On tensor train rank minimization: Statistical efficiency and scalable algorithm M Imaizumi, T Maehara, K Hayashi advances in neural information processing systems 30, 2017 | 40 | 2017 |
| 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 | 32 | 2015 |
| 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 | 32 | 2012 |
| Doubly decomposing nonparametric tensor regression M Imaizumi, K Hayashi International conference on machine learning, 727-736, 2016 | 31 | 2016 |
| Factorized asymptotic Bayesian hidden Markov models R Fujimaki, K Hayashi arXiv preprint arXiv:1206.4679, 2012 | 31 | 2012 |
| 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 | 28 | 2010 |
| Factorized asymptotic bayesian inference for latent feature models K Hayashi, R Fujimaki Advances in Neural Information Processing Systems 26, 2013 | 26 | 2013 |
| When Does Label Propagation Fail? A View from a Network Generative Model Y Yamaguchi, K Hayashi IJCAI, 2017 | 25 | 2017 |
| Tensor decomposition with smoothness M Imaizumi, K Hayashi International conference on machine learning, 1597-1606, 2017 | 21 | 2017 |
| A tractable fully bayesian method for the stochastic block model K Hayashi, T Konishi, T Kawamoto arXiv preprint arXiv:1602.02256, 2016 | 21 | 2016 |
| On random subsampling of Gaussian process regression: A graphon-based analysis K Hayashi, M Imaizumi, Y Yoshida International Conference on Artificial Intelligence and Statistics, 2055-2065, 2020 | 18 | 2020 |
| Dynamic exponential family matrix factorization K Hayashi, J Hirayama, S Ishii Pacific-Asia Conference on Knowledge Discovery and Data Mining, 452-462, 2009 | 17 | 2009 |
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
