Kohei Hayashi
Kohei Hayashi
Researcher, Preferred Networks
Verified email at preferred.jp - Homepage
Cited by
Cited by
Estimation of low-rank tensors via convex optimization
R Tomioka, K Hayashi, H Kashima
arXiv preprint arXiv:1010.0789, 2010
Statistical performance of convex tensor decomposition
R Tomioka, T Suzuki, K Hayashi, H Kashima
Advances in Neural Information Processing Systems (NIPS), 137, 2011
Tensor factorization using auxiliary information
A Narita, K Hayashi, R Tomioka, H Kashima
Data Mining and Knowledge Discovery 25 (2), 298-324, 2012
Making tree ensembles interpretable
S Hara, K Hayashi
arXiv preprint arXiv:1606.05390, 2016
Making tree ensembles interpretable: A bayesian model selection approach
S Hara, K Hayashi
International conference on artificial intelligence and statistics, 77-85, 2018
Cross-temporal link prediction
S Oyama, K Hayashi, H Kashima
2011 IEEE 11th International Conference on Data Mining, 1188-1193, 2011
On the extension of trace norm to tensors
R Tomioka, K Hayashi, H Kashima
NIPS Workshop on Tensors, Kernels, and Machine Learning 7, 2010
Expected tensor decomposition with stochastic gradient descent
T Maehara, K Hayashi, K Kawarabayashi
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 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
Factorized asymptotic bayesian inference for latent feature models
K Hayashi, R Fujimaki
Advances in Neural Information Processing Systems, 1214-1222, 2013
Factorized asymptotic Bayesian hidden Markov models
R Fujimaki, K Hayashi
arXiv preprint arXiv:1206.4679, 2012
Einconv: exploring unexplored tensor network decompositions for convolutional neural networks
K Hayashi, T Yamaguchi, Y Sugawara, S Maeda
arXiv preprint arXiv:1908.04471, 2019
On tensor train rank minimization: Statistical efficiency and scalable algorithm
M Imaizumi, T Maehara, K Hayashi
arXiv preprint arXiv:1708.00132, 2017
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
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
Doubly decomposing nonparametric tensor regression
M Imaizumi, K Hayashi
International Conference on Machine Learning, 727-736, 2016
A tractable fully bayesian method for the stochastic block model
K Hayashi, T Konishi, T Kawamoto
arXiv preprint arXiv:1602.02256, 2016
Dynamic exponential family matrix factorization
K Hayashi, JI Hirayama, S Ishii
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 452-462, 2009
When Does Label Propagation Fail? A View from a Network Generative Model
Y Yamaguchi, K Hayashi
IJCAI, 2017
Rebuilding factorized information criterion: Asymptotically accurate marginal likelihood
K Hayashi, S Maeda, R Fujimaki
International Conference on Machine Learning, 1358-1366, 2015
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