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Research

Publication (selected)
  • M.Imaizumi, T.Maehara, K.Hayashi (2017) "On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm". Advances in Neural Information and Processing Systems (NIPS 2017). To appear. arXiv 
  • M.Imaizumi, K.Hayashi (2017). Tensor Decomposition with Smoothness. In Journal of Machine Learning Research W & CP 70 (ICML 2017) paper
  • M.Imaizumi, R.Fujimaki (2017). Factorized Asymptotic Bayesian Policy Search for POMDPs. Proc. of 27th International Joint Conference of Artificial Intelligence (IJCAI 2017). paper
  • M.Imaizumi, K.Hayashi (2016). Doubly Decomposing Nonparametric Tensor Regression. In Journal of Machine Learning Research W & CP 48 (ICML 2016). paper arXiv
Preprint on arXiv
  • S.Hara, T.Katsuki, H.Yanagisawa, M.Imaizumi, T.Ono, R.Okamoto, S.Takeuchi, "Consistent Nonparametric Different-Feature Selection via the Sparsest k-Subgraph Problem". arXiv
  • M.Imaizumi, K.Kato, "A simple method to construct confidence bands in functional linear regression". arXiv R&R by Stat. Sinica.
  • M.Imaizumi, K.Kato, "PCA-based estimation for functional linear regression with functional responses". arXiv R&R by JMVA.
International Presentation
  • M.Imaizumi, T.Maehara, K.Hayashi "On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm". Neural Information and Processing Systems (NIPS), Long Beach, 2017/12.
  • M.Imaizumi, K.Kato, "A simple method to construct confidence bands in functional linear regression", Joint Meeting of 10th Asian Regional Section of the International Association for Statistical Computing and the NZ Statistical Association, New Zealand, 2017/12.
  • M.Imaizumi, K.Hayashi, ”Tensor Decomposition with Smoothness”, The International Conference on Machine Learning (ICML) 2017, Sydney, 2017/08.
  • M.Imaizumi, K.Hayashi, ”Tensor Decomposition with Smoothness”, Seminar in Data61, The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, 2017/08.
  • M.Imaizumi, R.Fujimaki, "Factorized Asymptotic Bayesian Policy Search for POMDPs", 27th International Joint Conference of Artificial Intelligence (IJCAI), Melbourne, 2017/08.
  • M.Imaizumi, K.Hayashi, "Tensor Decomposition with Smoothness", Neural Information Processing Systems workshop on Learning with Tensors (NIPS WS), Barcelona, 2016/12.
  • M.Imaizumi, "Nonlinear operator estimation with Bayes sieve prior", The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting (APRM), HongKong, 2016/07.
  • M.Imaizumi, "Regression with infinite dimensional spaces by reproducing kernel Hilbert space approach", 9th World Congress in Probability and Statistics (WCPS), Toronto, 2016/07.
  • M.Imaizumi, K.Hayashi, ”Doubly Decomposing Nonparametric Tensor Regression”, The International Conference on Machine Learning (ICML) 2016, New York, 2016/06.
  • M.Imaizumi, "Nonparametric multivariate regression with tensor product RKHS", International Meeting on High-Dimensional Data Driven Science (HD3), Kyoto, 2015/12.
  • M.Imaizumi, ”Efficient estimation for semiparametric models by reproducing kernel Hilbert space”, American statistical association, Joint statistical meeting (JSM), Seattle, 2015/08.
  • M.Imaizumi, K.Hayashi, ”Bayesian estimation for nonparametric regression with low-rank tensor data”, 10th Conference on Bayesian Nonparametrics (BNP), Raleigh, 2015/06.
  • M.Imaizumi, ”Efficient estimation for semiparametric models by reproducing kernel Hilbert space, STICERD Econometrics Seminar Series (LSE), London, 2014/12.
  • M.Imaizumi, ”An approximation method for discrete Markov decision models with high dimensional state space”, Econometric Society European Winter Meeting, Madrid, 2014/12.
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