フォロー
Motonobu Kanagawa
タイトル
引用先
引用先
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
3292018
Large sample analysis of the median heuristic
D Garreau, W Jitkrittum, M Kanagawa
arXiv preprint arXiv:1707.07269, 2017
1322017
Convergence guarantees for kernel-based quadrature rules in misspecified settings
M Kanagawa, BK Sriperumbudur, K Fukumizu
Advances in Neural Information Processing Systems 29, 2016
552016
Convergence analysis of deterministic kernel-based quadrature rules in misspecified settings
M Kanagawa, BK Sriperumbudur, K Fukumizu
Foundations of Computational Mathematics 20, 155-194, 2020
402020
Convergence guarantees for adaptive Bayesian quadrature methods
M Kanagawa, P Hennig
Advances in Neural Information Processing Systems 32, 2019
362019
Counterfactual mean embeddings
K Muandet, M Kanagawa, S Saengkyongam, S Marukatat
Journal of Machine Learning Research 22 (162), 1-71, 2021
342021
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
232016
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
172021
Kernel recursive ABC: Point estimation with intractable likelihood
T Kajihara, M Kanagawa, K Yamazaki, K Fukumizu
International Conference on Machine Learning, 2400-2409, 2018
172018
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
162014
On the positivity and magnitudes of Bayesian quadrature weights
T Karvonen, M Kanagawa, S Särkkä
Statistics and Computing 29, 1317-1333, 2019
132019
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, 350-370, 2017
112017
Simulator calibration under covariate shift with kernels
K Kisamori, M Kanagawa, K Yamazaki
International Conference on Artificial Intelligence and Statistics, 1244-1253, 2020
102020
Improved random features for dot product kernels
J Wacker, M Kanagawa, M Filippone
arXiv preprint arXiv:2201.08712, 2022
82022
Intergenerational risk sharing in a defined contribution pension system: analysis with Bayesian optimization
A Chen, M Kanagawa, F Zhang
ASTIN Bulletin: The Journal of the IAA 53 (3), 515-544, 2023
7*2023
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
62020
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
D Gogolashvili, M Zecchin, M Kanagawa, M Kountouris, M Filippone
arXiv preprint arXiv:2303.04020, 2023
32023
Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood
M Naslidnyk, M Kanagawa, T Karvonen, M Mahsereci
arXiv preprint arXiv:2307.07466, 2023
22023
Empirical representations of probability distributions via kernel mean embeddings
M Kanagawa
Mar, 2016
22016
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
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