Hayashi, Kohei

26 publications

NeurIPS 2025 Pairwise Optimal Transports for Training All-to-All Flow-Based Condition Transfer Model Kotaro Ikeda, Masanori Koyama, Jinzhe Zhang, Kohei Hayashi, Kenji Fukumizu

ICLR 2024 Neural Fourier Transform: A General Approach to Equivariant Representation Learning Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato

ICLRW 2023 TabRet: Pre-Training Transformer-Based Tabular Models for Unseen Columns Soma Onishi, Kenta Oono, Kohei Hayashi

ECML-PKDD 2022 A Scaling Law for Syn2real Transfer: How Much Is Your Pre-Training Effective? Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi

ICMLW 2022 Learning Switchable Representation with Masked Decoding and Sparse Encoding Kohei Hayashi, Masanori Koyama

AISTATS 2020 On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida

ICLRW 2019 Data Interpolating Prediction: Alternative Interpretation of Mixup Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi, Sosuke Kobayashi

NeurIPS 2019 Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda

AISTATS 2018 Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach Satoshi Hara, Kohei Hayashi

IJCAI 2018 Think Globally, Embed Locally - Locally Linear Meta-Embedding of Words Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi

NeurIPS 2017 Fitting Low-Rank Tensors in Constant Time Kohei Hayashi, Yuichi Yoshida

NeurIPS 2017 On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi

IJCAI 2017 Tensor Decomposition with Missing Indices Yuto Yamaguchi, Kohei Hayashi

ICML 2017 Tensor Decomposition with Smoothness Masaaki Imaizumi, Kohei Hayashi

IJCAI 2017 When Does Label Propagation Fail? a View from a Network Generative Model Yuto Yamaguchi, Kohei Hayashi

ICML 2016 Doubly Decomposing Nonparametric Tensor Regression Masaaki Imaizumi, Kohei Hayashi

AAAI 2016 Expected Tensor Decomposition with Stochastic Gradient Descent Takanori Maehara, Kohei Hayashi, Ken-ichi Kawarabayashi

IJCAI 2016 Identifying Key Observers to Find Popular Information in Advance Takuya Konishi, Tomoharu Iwata, Kohei Hayashi, Ken-ichi Kawarabayashi

NeurIPS 2016 Minimizing Quadratic Functions in Constant Time Kohei Hayashi, Yuichi Yoshida

ACML 2015 Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias Yohei Kondo, Shin-ichi Maeda, Kohei Hayashi

ICML 2015 Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki

NeurIPS 2013 Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki

ICML 2012 Factorized Asymptotic Bayesian Hidden Markov Models Ryohei Fujimaki, Kohei Hayashi

NeurIPS 2012 Weighted Likelihood Policy Search with Model Selection Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara

NeurIPS 2011 Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima

ECML-PKDD 2011 Tensor Factorization Using Auxiliary Information Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima