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
1002018
Convergence guarantees for kernel-based quadrature rules in misspecified settings
M Kanagawa, BK Sriperumbudur, K Fukumizu
Advances in Neural Information Processing Systems, 3288-3296, 2016
432016
Large sample analysis of the median heuristic
D Garreau, W Jitkrittum, M Kanagawa
arXiv preprint arXiv:1707.07269, 2017
392017
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
372020
Convergence guarantees for adaptive Bayesian quadrature methods
M Kanagawa, P Hennig
arXiv preprint arXiv:1905.10271, 2019
142019
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
142016
Monte Carlo filtering using kernel embedding of distributions
M Kanagawa, Y Nishiyama, A Gretton, K Fukumizu
Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014
112014
Kernel recursive ABC: Point estimation with intractable likelihood
T Kajihara, M Kanagawa, K Yamazaki, K Fukumizu
International Conference on Machine Learning, 2400-2409, 2018
82018
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
82017
Counterfactual mean embeddings
K Muandet, M Kanagawa, S Saengkyongam, S Marukatat
arXiv preprint arXiv:1805.08845, 2018
72018
Simulator calibration under covariate shift with kernels
K Kisamori, M Kanagawa, K Yamazaki
International Conference on Artificial Intelligence and Statistics, 1244-1253, 2020
6*2020
On the positivity and magnitudes of Bayesian quadrature weights
T Karvonen, M Kanagawa, S Särkkä
Statistics and Computing 29 (6), 1317-1333, 2019
62019
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
12020
Empirical representations of probability distributions via kernel mean embeddings
M Kanagawa
12016
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
Intergenerational Risk Sharing in a Collective Defined-Contribution Pension System: A Simulation Study with Bayesian Optimization
A Chen, M Kanagawa, F Zhang
Available at SSRN 3873751, 2021
2021
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
2021
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
Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference
K Muandet, M Kanagawa, S Saengkyongam, S Marukatat
Machine Learning for Causal Inference, Counterfactual Prediction, and …, 2018
2018
Distributional Statistics Estimation via Kernel Mean Embeddings--Density Function, Credible interval, and Moment Estimation
M Kanagawa, K Fukumizu
IEICE Technical Report; IEICE Tech. Rep. 113 (286), 147-154, 2013
2013
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Articles 1–20