Motonobu Kanagawa
Title
Cited by
Cited by
Year
Gaussian processes and kernel methods: A review on connections and equivalences
M Kanagawa, P Hennig, D Sejdinovic, BK Sriperumbudur
arXiv preprint arXiv:1807.02582, 2018
642018
Convergence guarantees for kernel-based quadrature rules in misspecified settings
M Kanagawa, BK Sriperumbudur, K Fukumizu
arXiv preprint arXiv:1605.07254, 2016
362016
Convergence analysis of deterministic kernel-based quadrature rules in misspecified settings
M Kanagawa, BK Sriperumbudur, K Fukumizu
Foundations of Computational Mathematics 20 (1), 155-194, 2020
292020
Large sample analysis of the median heuristic
D Garreau, W Jitkrittum, M Kanagawa
arXiv preprint arXiv:1707.07269, 2017
252017
Filtering with state-observation examples via kernel monte carlo filter
M Kanagawa, Y Nishiyama, A Gretton, K Fukumizu
Neural computation 28 (2), 382-444, 2016
152016
Convergence guarantees for adaptive Bayesian quadrature methods
M Kanagawa, P Hennig
arXiv preprint arXiv:1905.10271, 2019
122019
Monte Carlo filtering using kernel embedding of distributions
M Kanagawa, Y Nishiyama, A Gretton, K Fukumizu
Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014
102014
Kernel recursive ABC: Point estimation with intractable likelihood
T Kajihara, M Kanagawa, K Yamazaki, K Fukumizu
International Conference on Machine Learning, 2400-2409, 2018
72018
On the positivity and magnitudes of Bayesian quadrature weights
T Karvonen, M Kanagawa, S Särkkä
Statistics and Computing 29 (6), 1317-1333, 2019
62019
Unsupervised group matching with application to cross-lingual topic matching without alignment information
T Iwata, M Kanagawa, T Hirao, K Fukumizu
Data mining and knowledge discovery 31 (2), 350-370, 2017
62017
Counterfactual mean embeddings
K Muandet, M Kanagawa, S Saengkyongam, S Marukatat
arXiv preprint arXiv:1805.08845, 2018
52018
Intractable Likelihood Regression for Covariate Shift by Kernel Mean Embedding.
K Kisamori, K Yamazaki
arXiv preprint arXiv:1809.08159, 2018
4*2018
Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models
Y Nishiyama, M Kanagawa, A Gretton, K Fukumizu
arXiv preprint arXiv:1409.5178, 2014
12014
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models
Y Nishiyama, M Kanagawa, A Gretton, K Fukumizu
Machine Learning 109 (5), 939-972, 2020
2020
Model Selection for Simulator-based Statistical Models: A Kernel Approach
T Kajihara, M Kanagawa, Y Nakaguchi, K Khandelwal, K Fukumiziu
arXiv preprint arXiv:1902.02517, 2019
2019
Empirical representations of probability distributions via kernel mean embeddings
M Kanagawa
2016
Model-based Kernel Sum Rule with Applications to State Space Models
Y Nishiyama, M Kanagawa, A Gretton, K Fukumizu
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Articles 1–17