Publications
Preprints:
K. Mitsuzawa, M. Kanagawa, S. Bortoli, M. Grossi, P. Papotti, "Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison", arXiv:2311.01537, 2023 [https://arxiv.org/abs/2311.01537]
M. Naslidnyk, M. Kanagawa, T. Karvonen, M. Mahsereci, "Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood", arXiv:2307.07466, 2023. [https://arxiv.org/abs/2307.07466]
D. Gogolashvili, M. Zecchin, M. Kanagawa, M. Kountouris, M. Filippone, "When is Importance Weighting Correction Needed for Covariate Shift Adaptation?", arXiv:2303.04020, 2023. [https://arxiv.org/abs/2303.04020]
J. Wacker, M. Kanagawa and M. Filippone, "Improved Random Features for Dot Product Kernels". [https://arxiv.org/abs/2201.08712]
V. Weit, M. Kanagawa and D. Sejdinovic, "Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes", arXiv:2106.01121 [stat.ML]. [https://arxiv.org/abs/2106.01121]
T. Kajihara, M. Kanagawa, Y. Nakaguchi, K. Khandelwal and K. Fukumizu, "Model Selection for Simulator-based Statistical Models: A Kernel Approach", arXiv:1902.02517 [stat.ML]. [https://arxiv.org/abs/1902.02517]
D. Garreau, W. Jitkrittum and M. Kanagawa, "Large sample analysis of the median heuristic", arXiv:1707.07269 [math.ST]. [https://arxiv.org/abs/1707.07269]
M. Kanagawa, P. Hennig, D. Sejdinovic and B. K. Sriperumbudur, "Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences", arXiv:1807.02582 [stats.ML]. [https://arxiv.org/abs/1807.02582]
Published papers:
A Chen, M Kanagawa, F Zhang, "Intergenerational risk sharing in a defined contribution pension system: analysis with Bayesian optimization", ASTIN Bulletin: The Journal of the IAA, pages 1-30, 2023. [https://arxiv.org/abs/2106.13644]
K. Muandet, M. Kanagawa, S. Saengkyongam, S. Marukatat, "Counterfactual Mean Embeddings", Journal of Machine Learning Research; 22(162):1−71, 2021. [https://arxiv.org/abs/1805.08845]
K. Kisamori, M. Kanagawa and K. Yamazaki "Simulator Calibration under Covariate Shift with Kernels", AISTATS 2020. arXiv:1809.08159 [stat.ML]. [https://arxiv.org/abs/1809.08159]
Y. Nishiyama, M. Kanagawa, A. Gretton, and K. Fukumizu, "Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models," Machine Learning, 2020. arXiv:1409.5178 [stat.ML]. [https://arxiv.org/abs/1409.5178]
M. Kanagawa, B. K. Sriperumbudur and K. Fukumizu, "Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings," Foundations of Computational Mathematics, 2020. arXiv:1709.00147 [math.NA]. [https://arxiv.org/abs/1709.00147]
M. Kanagawa and P. Hennig "Convergence Guarantees for Adaptive Bayesian Quadrature Methods", NeurIPS 2019. arXiv:1905.10271 [stat.ML]. [https://arxiv.org/abs/1905.10271]
T. Karvonen, M. Kanagawa and S. Särkkä "On the positivity and magnitudes of Bayesian quadrature weights" Statistics and Computing, 2019. arXiv:1812.08509 [math.ST]. [https://arxiv.org/abs/1812.08509]
T. Kajihara, M. Kanagawa, K. Yamazaki, and K. Fukumizu, "Kernel Recursive ABC: Point Estimation with Intractable Likelihood," Thirty-fifth International Conference on Machine Learning (ICML 2018). arXiv:1802.08404 [stat.ML]. [https://arxiv.org/abs/1802.08404]
T. Iwata, M. Kanagawa, T. Hirao, K. Fukumizu, "Unsupervised Group Matching with Application to Cross-lingual Topic Matching without Alignment Information," Data Mining and Knowledge Discovery, volume 31, issue 2, pages 350-370, 2017. [paper]
M. Kanagawa, B. K. Sriperumbudur and K. Fukumizu, "Convergence guarantees for kernel-based quadrature rules in misspecified settings," Advances in Neural Information Processing Systems 29, pages 3288–3296, 2016. [arXiv:1605.07254]
M. Kanagawa, Y. Nishiyama, A. Gretton and K. Fukumizu, "Filtering with State-Observation Examples via Kernel Monte Carlo Filter," Neural Computation, volume 28, issue 2, pages 382–444, 2016. [paper] [arxiv:1312.4664] [demo]
M. Kanagawa, Y. Nishiyama, A. Gretton, and K. Fukumizu, "Monte Carlo Filtering using Kernel Embedding of Distributions," Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14), pages 1987-1903, 2014. [paper] [supplements]
M. Kanagawa and K. Fukumizu, "Recovering Distributions from Gaussian RKHS Embeddings," Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), pages 457-465, 2014. [paper] [supplements] (Note: There is a mistake in the proof of Theorem 1 (the derivation of the upper bound of Eq. 16). A fixed version will be available soon, but the resulting rate may significantly change from the current one. So please do not cite this version. For more general results, please look at Theorem 1 of arXiv:1605.07254 (June 2016).)