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Motonobu Kanagawa
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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
1842018
Large sample analysis of the median heuristic
D Garreau, W Jitkrittum, M Kanagawa
arXiv preprint arXiv:1707.07269, 2017
682017
Convergence guarantees for kernel-based quadrature rules in misspecified settings
M Kanagawa, BK Sriperumbudur, K Fukumizu
Advances in Neural Information Processing Systems 29, 2016
382016
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
322020
Convergence guarantees for adaptive Bayesian quadrature methods
M Kanagawa, P Hennig
Advances in Neural Information Processing Systems 32, 2019
222019
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
222016
Kernel recursive ABC: Point estimation with intractable likelihood
T Kajihara, M Kanagawa, K Yamazaki, K Fukumizu
International Conference on Machine Learning, 2400-2409, 2018
142018
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
142014
Counterfactual Mean Embeddings.
K Muandet, M Kanagawa, S Saengkyongam, S Marukatat
J. Mach. Learn. Res. 22, 162:1-162:71, 2021
112021
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
102017
Simulator calibration under covariate shift with kernels
K Kisamori, M Kanagawa, K Yamazaki
International Conference on Artificial Intelligence and Statistics, 1244-1253, 2020
72020
On the positivity and magnitudes of Bayesian quadrature weights
T Karvonen, M Kanagawa, S Särkkä
Statistics and Computing 29 (6), 1317-1333, 2019
72019
Connections and Equivalences between the Nystr\" om Method and Sparse Variational Gaussian Processes
V Wild, M Kanagawa, D Sejdinovic
arXiv preprint arXiv:2106.01121, 2021
52021
Gaussian processes and kernel methods: a review on connections and equivalences., 2018
M Kanagawa, P Hennig, D Sejdinovic, BK Sriperumbudur
arXiv preprint arXiv:1807.02582, 1807
51807
Improved Random Features for Dot Product Kernels
J Wacker, M Kanagawa, M Filippone
arXiv preprint arXiv:2201.08712, 2022
32022
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
32014
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
22020
Empirical representations of probability distributions via kernel mean embeddings
M Kanagawa
12016
Intergenerational risk sharing in a collective defined contribution pension system: a simulation study with Bayesian optimization
A Chen, M Kanagawa, F Zhang
arXiv preprint arXiv:2106.13644, 2021
2021
Intergenerational Risk Sharing in a Defined Contribution Pension System: Analysis with Bayesian Optimization
A Chen, M Kanagawa, F Zhang
HAL Post-Print, 2021
2021
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Articles 1–20