| 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 | 382 | 2018 |
| Large sample analysis of the median heuristic D Garreau, W Jitkrittum, M Kanagawa arXiv preprint arXiv:1707.07269, 2017 | 147 | 2017 |
| Convergence guarantees for kernel-based quadrature rules in misspecified settings M Kanagawa, BK Sriperumbudur, K Fukumizu Advances in Neural Information Processing Systems 29, 2016 | 53 | 2016 |
| 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 | 43 | 2020 |
| Counterfactual mean embeddings K Muandet, M Kanagawa, S Saengkyongam, S Marukatat Journal of Machine Learning Research 22 (162), 1-71, 2021 | 40 | 2021 |
| Convergence guarantees for adaptive Bayesian quadrature methods M Kanagawa, P Hennig Advances in neural information processing systems 32, 2019 | 37 | 2019 |
| 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 | 24 | 2016 |
| 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 | 20 | 2021 |
| 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 | 17 | 2014 |
| Kernel recursive ABC: Point estimation with intractable likelihood T Kajihara, M Kanagawa, K Yamazaki, K Fukumizu International Conference on Machine Learning, 2400-2409, 2018 | 16 | 2018 |
| On the positivity and magnitudes of Bayesian quadrature weights T Karvonen, M Kanagawa, S Särkkä Statistics and Computing 29, 1317-1333, 2019 | 14 | 2019 |
| 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 | 12 | 2017 |
| Simulator calibration under covariate shift with kernels K Kisamori, M Kanagawa, K Yamazaki International Conference on Artificial Intelligence and Statistics, 1244-1253, 2020 | 11 | 2020 |
| Improved random features for dot product kernels J Wacker, M Kanagawa, M Filippone arXiv preprint arXiv:2201.08712, 2022 | 8 | 2022 |
| 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 | 7 | 2020 |
| 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 | 6 | 2023 |
| 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 | 4 | 2023 |
| Empirical representations of probability distributions via kernel mean embeddings M Kanagawa Mar, 2016 | 2 | 2016 |
| Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison K Mitsuzawa, M Kanagawa, S Bortoli, M Grossi, P Papotti arXiv preprint arXiv:2311.01537, 2023 | 1 | 2023 |
Kenji FukumizuThe Institute of Statistical MathematicsVerified email at ism.ac.jp
