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Heishiro Kanagawa
Heishiro Kanagawa
Verified email at ncl.ac.uk - Homepage
Title
Cited by
Cited by
Year
Cross-domain recommendation via deep domain adaptation
H Kanagawa, H Kobayashi, N Shimizu, Y Tagami, T Suzuki
European Conference on Information Retrieval, 20-29, 2019
792019
Deep proxy causal learning and its application to confounded bandit policy evaluation
L Xu, H Kanagawa, A Gretton
Advances in Neural Information Processing Systems 34, 26264-26275, 2021
322021
Blindness of score-based methods to isolated components and mixing proportions
LK Wenliang, H Kanagawa
arXiv preprint arXiv:2008.10087, 2020
292020
Informative features for model comparison
W Jitkrittum, H Kanagawa, P Sangkloy, J Hays, B Schölkopf, A Gretton
Advances in neural information processing systems 31, 2018
292018
Gaussian process nonparametric tensor estimator and its minimax optimality
H Kanagawa, T Suzuki, H Kobayashi, N Shimizu, Y Tagami
International Conference on Machine Learning, 1632-1641, 2016
242016
Testing Goodness of Fit of Conditional Density Models with Kernels
W Jitkrittum, H Kanagawa, B Schölkopf
Uncertainty in Artificial Intelligence, 221-230, 2020
212020
Minimax optimal alternating minimization for kernel nonparametric tensor learning
T Suzuki, H Kanagawa, H Kobayashi, N Shimizu, Y Tagami
Advances in Neural Information Processing Systems 29, 2016
212016
A kernel Stein test for comparing latent variable models
H Kanagawa, W Jitkrittum, L Mackey, K Fukumizu, A Gretton
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023
142023
Cross-domain recommender systems using domain separation networks and autoencoders
H Kanagawa, H Kobayashi, N Shimizu, Y Tagami
US Patent 11,699,095, 2023
72023
A kernel stein test of goodness of fit for sequential models
J Baum, H Kanagawa, A Gretton
International Conference on Machine Learning, 1936-1953, 2023
42023
Amortised Learning by Wake-Sleep
LK Wenliang, T Moskovitz, H Kanagawa, M Sahani
International Conference on Machine Learning, 10236-10247, 2020
42020
Controlling moments with kernel Stein discrepancies
H Kanagawa, A Barp, A Gretton, L Mackey
arXiv preprint arXiv:2211.05408, 2022
32022
Stein -Importance Sampling
C Wang, Y Chen, H Kanagawa, CJ Oates
Advances in Neural Information Processing Systems 36, 2024
2024
Statistical Model Evaluation Using Reproducing Kernels and Stein’s method
H Kanagawa
UCL (University College London), 2022
2022
Non-parametric tensor learning with Gaussian process prior and its application to multi-task learning
金川平志郎, 鈴木大慈
電子情報通信学会技術研究報告= IEICE technical report: 信学技報 115 (323 …, 2015
2015
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